ieee projects 2012 2013 - bio informatics

Elysium Technologies Private Limited Approved by ISO 9001:2008 and AICTE for SKP Training Singapore | Madurai | Trichy | Coimbatore | Cochin | Kollam | Chennai http://www.elysiumtechnologies.com, [email protected]

IEEE Final Year Projects 2012 |Student Projects | Bio-informatics Projects

IEEE FINAL YEAR PROJECTS 2012 – 2013

BIO- INFORMATICS

Corporate Office: Madurai

227-230, Church road, Anna nagar, Madurai – 625 020.

0452 – 4390702, 4392702, +9199447933980

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Website: www.elysiumtechnologies.com

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15, III Floor, SI Towers, Melapudur main road, Trichy – 620 001.

0431 – 4002234, +919790464324.

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Email: [email protected].


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Floor, Anjali Complex, near south over bridge, Valanjampalam,

Cochin – 682 016, Kerala.

0484 – 6006002, +917736004002.

Email: [email protected], Website: www.elysiumtechnologies.com

mailto:[email protected]









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BIO - INFORMATICS 2012 - 2013

In the biological domain, clustering is based on the assumption that genes or metabolites involved in a common

biological process are coexpressed/coaccumulated under the control of the same regulatory network. Thus, a detailed

inspection of the grouped patterns to verify their memberships to well-known metabolic pathways could be very useful

for the evaluation of clusters from a biological perspective. The aim of this work is to propose a novel approach for the

comparison of clustering methods over metabolic data sets, including prior biological knowledge about the relation

among elements that constitute the clusters. A way of measuring the biological significance of clustering solutions is

proposed. This is addressed from the perspective of the usefulness of the clusters to identify those patterns that change

in coordination and belong to common pathways of metabolic regulation. The measure summarizes in a compact way

the objective analysis of clustering methods, which respects coherence and clusters distribution. It also evaluates the

biological internal connections of such clusters considering common pathways. The proposed measure was tested in

two biological databases using three clustering methods.

Several approaches have been presented in the literature to cluster Protein-Protein Interaction (PPI) networks. They can

be grouped in two main categories: those allowing a protein to participate in different clusters and those generating only

nonoverlapping clusters. In both cases, a challenging task is to find a suitable compromise between the biological

relevance of the results and a comprehensive coverage of the analyzed networks. Indeed, methods returning high

accurate results are often able to cover only small parts of the input PPI network, especially when low-characterized

networks are considered. We present a coclustering-based technique able to generate both overlapping and

nonoverlapping clusters. The density of the clusters to search for can also be set by the user. We tested our method on

the two networks of yeast and human, and compared it to other five well-known techniques on the same interaction data

sets. The results showed that, for all the examples considered, our approach always reaches a good compromise

between accuracy and network coverage. Furthermore, the behavior of our algorithm is not influenced by the structure

of the input network, different from all the techniques considered in the comparison, which returned very good results

on the yeast network, while on the human network their outcomes are rather poor.

A Biologically Inspired Validity Measure for Comparison of Clustering Methods over

Metabolic Data Sets

A Co-Clustering Approach for Mining Large Protein-Protein Interaction Networks

A Comparative Study on Filtering Protein Secondary Structure Prediction



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Filtering of Protein Secondary Structure Prediction (PSSP) aims to provide physicochemically realistic results, while it

usually improves the predictive performance. We performed a comparative study on this challenging problem, utilizing

both machine learning techniques and empirical rules and we found that combinations of the two lead to the highest

improvement.

Recently, several domain-based computational models for predicting protein-protein interactions (PPIs) have been

proposed. The conventional methods usually infer domain or domain combination (DC) interactions from already known

interacting sets of proteins, and then predict PPIs using the information. However, the majority of these models often

have limitations in providing detailed information on which domain pair (single domain interaction) or DC pair

(multidomain interaction) will actually interact for the predicted protein interaction. Therefore, a more comprehensive

and concrete computational model for the prediction of PPIs is needed. We developed a computational model to predict

PPIs using the information of intraprotein domain cohesion and interprotein DC coupling interaction. A method of

identifying the primary interacting DC pair was also incorporated into the model in order to infer actual participants in a

predicted interaction. Our method made an apparent improvement in the PPI prediction accuracy, and the primary

interacting DC pair identification was valid specifically in predicting multidomain protein interactions. In this paper, we

demonstrate that 1) the intraprotein domain cohesion is meaningful in improving the accuracy of domain-based PPI

prediction, 2) a prediction model incorporating the intradomain cohesion enables us to identify the primary interacting

DC pair, and 3) a hybrid approach using the intra/interdomain interaction information can lead to a more accurate

prediction.

The functional annotation of proteins is one of the most important tasks in the post-genomic era. Although many

computational approaches have been developed in recent years to predict protein function, most of these traditional

algorithms do not take interrelationships among functional terms into account, such as different GO terms usually

coannotate with some common proteins. In this study, we propose a new functional similarity measure in the form of

Jaccard coefficient to quantify these interrelationships and also develop a framework for incorporating GO term

similarity into protein function prediction process. The experimental results of cross-validation on S. cerevisiae and

Homo sapiens data sets demonstrate that our method is able to improve the performance of protein function

prediction. In addition, we find that small size terms associated with a few of proteins obtain more benefit than the large

size ones when considering functional interrelationships. We also compare our similarity measure with other two

widely used measures, and results indicate that when incorporated into function prediction algorithms, our proposed

measure is more effective. Experiment results also illustrate that our algorithms outperform two previous competing

A Computational Model for Predicting Protein Interactions Based on Multidomain

Collaboration

A Framework for Incorporating Functional Interrelationships into Protein Function

Prediction Algorithms



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algorithms, which also take functional interrelationships into account, in prediction accuracy. Finally, we show that our

method is robust to annotations in the database which are not complete at present. These results give new insights

about the importance of functional interrelationships in protein function prediction.

In medical society, the prognostic models, which use clinicopathologic features and predict prognosis after a certain

treatment, have been externally validated and used in practice. In recent years, most research has focused on high

dimensional genomic data and small sample sizes. Since clinically similar but molecularly heterogeneous tumors may

produce different clinical outcomes, the combination of clinical and genomic information, which may be complementary,

is crucial to improve the quality of prognostic predictions. However, there is a lack of an integrating scheme for clinic-

genomic models due to the {rm P}gg{rm N} problem, in particular, for a parsimonious model. We propose a methodology

to build a reduced yet accurate integrative model using a hybrid approach based on the Cox regression model, which

uses several dimension reduction techniques, {rm L}_{2} penalized maximum likelihood estimation (PMLE), and

resampling methods to tackle the problem. The predictive accuracy of the modeling approach is assessed by several

metrics via an independent and thorough scheme to compare competing methods. In breast cancer data studies on a

metastasis and death event, we show that the proposed methodology can improve prediction accuracy and build a final

model with a hybrid signature that is parsimonious when integrating both types of variables.

In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is

proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available

short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical

constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to

considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic

modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization

algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to

cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding

a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the

parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much

improved performance over the traditional EKF method.

A Hybrid EKF and Switching PSO Algorithm for Joint State and Parameter Estimation of

Lateral Flow Immunoassay Models

A Memory Efficient Method for Structure-Based RNA Multiple Alignment

A Hybrid Approach to Survival Model Building Using Integration of Clinical and Molecular

Information in Censored Data



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Structure-based RNA multiple alignment is particularly challenging because covarying mutations make sequence

information alone insufficient. Existing tools for RNA multiple alignment first generate pairwise RNA structure

alignments and then build the multiple alignment using only sequence information. Here we present PMFastR, an

algorithm which iteratively uses a sequence-structure alignment procedure to build a structure-based RNA multiple

alignment from one sequence with known structure and a database of sequences from the same family. PMFastR also

has low memory consumption allowing for the alignment of large sequences such as 16S and 23S rRNA. The algorithm

also provides a method to utilize a multicore environment. We present results on benchmark data sets from BRAliBase,

which shows PMFastR performs comparably to other state-of-the-art programs. Finally, we regenerate 607 Rfam seed

alignments and show that our automated process creates multiple alignments similar to the manually curated Rfam seed

alignments. Thus, the techniques presented in this paper allow for the generation of multiple alignments using

sequence-structure guidance, while limiting memory consumption. As a result, multiple alignments of long RNA

sequences, such as 16S and 23S rRNAs, can easily be generated locally on a personal computer. The software and

supplementary data are available at http://genome.ucf.edu/PMFastR.

Comparing two or more phylogenetic trees is a fundamental task in computational biology. The simplest outcome of

such a comparison is a pairwise measure of similarity, dissimilarity, or distance. A large number of such measures have

been proposed, but so far all suffer from problems varying from computational cost to lack of robustness; many can be

shown to behave unexpectedly under certain plausible inputs. For instance, the widely used Robinson-Foulds distance

is poorly distributed and thus affords little discrimination, while also lacking robustness in the face of very small

changes—reattaching a single leaf elsewhere in a tree of any size can instantly maximize the distance. In this paper, we

introduce a new pairwise distance measure, based on matching, for phylogenetic trees. We prove that our measure

induces a metric on the space of trees, show how to compute it in low polynomial time, verify through statistical testing

that it is robust, and finally note that it does not exhibit unexpected behavior under the same inputs that cause problems

with other measures. We also illustrate its usefulness in clustering trees, demonstrating significant improvements in the

quality of hierarchical clustering as compared to the same collections of trees clustered using the Robinson-Foulds

distance.

Identifying conserved gene clusters is an important step toward understanding the evolution of genomes and predicting

the functions of genes. A famous model to capture the essential biological features of a conserved gene cluster is called

the gene-team model. The problem of finding the gene teams of two general sequences is the focus of this paper. For

this problem, He and Goldwasser had an efficient algorithm that requires O(mn) time using O(m + n) working space,

where m and n are, respectively, the numbers of genes in the two given sequences. In this paper, a new efficient

A Metric for Phylogenetic Trees Based on Matching

A New Efficient Algorithm for the Gene-Team Problem on General Sequences



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algorithm is presented. Assume m ≤ n. Let C = ΣαϵΣ o1(α)o2(α), where Σ is the set of distinct genes, and o1(α) and o2(a)

are, respectively, the numbers of copies of a in the two given sequences. Our new algorithm requires O(min{C lg n,mn})

time using O(m + n) working space. As compared with He and Goldwasser's algorithm, our new algorithm is more

practical, as C is likely to be much smaller than mn in practice. In addition, our new algorithm is output sensitive. Its

running time is O(lg n) times the size of the output. Moreover, our new algorithm can be efficiently extended to find the

gene teams of k general sequences in O(k C Ig (n1n2...nk)) time, where ni is the number of genes in the ith input

sequence.

Today's genome analysis applications require sequence representations allowing for fast access to their contents while

also being memory-efficient enough to facilitate analyses of large-scale data. While a wide variety of sequence

representations exist, lack of a generic implementation of efficient sequence storage has led to a plethora of poorly

reusable or programming language- specific implementations. We present a novel, space-efficient data structure

(GtEncseq) for storing multiple biological sequences of variable alphabet size, with customizable character

transformations, wildcard support, and an assortment of internal representations optimized for different distributions of

wildcards and sequence lengths. For the human genome (3.1 gigabases, including 237 million wildcard characters) our

representation requires only 2 + 8 · 10-6 bits per character. Implemented in C, our portable software implementation

provides a variety of methods for random and sequential access to characters and substrings (including different

reading directions) using an object-oriented interface. In addition, it includes access to metadata like sequence

descriptions or character distributions. The library is extensible to be used from various scripting languages. GtEncseq

is much more versatile than previous solutions, adding features that were previously unavailable. Benchmarks show

that it is competitive with respect to space and time requirements

Feature selection is widely established as one of the fundamental computational techniques in mining microarray data.

Due to the lack of categorized information in practice, unsupervised feature selection is more practically important but

correspondingly more difficult. Motivated by the cluster ensemble techniques, which combine multiple clustering

solutions into a consensus solution of higher accuracy and stability, recent efforts in unsupervised feature selection

proposed to use these consensus solutions as oracles. However, these methods are dependent on both the particular

cluster ensemble algorithm used and the knowledge of the true cluster number. These methods will be unsuitable when

the true cluster number is not available, which is common in practice. In view of the above problems, a new

unsupervised feature ranking method is proposed to evaluate the importance of the features based on consensus

affinity. Different from previous works, our method compares the corresponding affinity of each feature between a pair

of instances based on the consensus matrix of clustering solutions. As a result, our method alleviates the need to know

the true number of clusters and the dependence on particular cluster ensemble approaches as in previous works.

A New Unsupervised Feature Ranking Method for Gene Expression Data Based on

Consensus Affinity

A New Efficient Data Structure for Storage and Retrieval of Multiple Biosequences



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Experiments on real gene expression data sets demonstrate significant improvement of the feature ranking results when

compared to several state-of-the-art techniques.

The influence of DNA cis-regulatory elements on a gene's expression has been intensively studied. However, little is

known about expressions driven by trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are

recognized to be important in cancer as they influence multiple genes on a global scale. The challenge in detecting

trans-effects is mainly due to the computational difficulty in detecting weak and sparse trans-acting signals amidst co-

occuring passenger events. We propose an integrative approach to learn a sparse interaction network of DNA copy-

number regions with their downstream targets in a breast cancer dataset. Information from this network helps

distinguish copy-number driven from copy-number independent expression changes on a global scale. Our result

further delineates cis- and trans-effects in a breast cancer dataset, for which important oncogenes such as ESR1 and

ERBB2 appear to be highly copy-number dependent. Further, our model is shown to be efficient and in terms of

goodness of fit no worse than other state-of the art predictors and network reconstruction models using both simulated

and real data.

Despite years of research, the name ambiguity problem remains largely unresolved. Outstanding issues include how to

A plenitude of feature selection (FS) methods is available in the literature, most of them rising as a need to analyze data

of very high dimension, usually hundreds or thousands of variables. Such data sets are now available in various

application areas like combinatorial chemistry, text mining, multivariate imaging, or bioinformatics. As a general

accepted rule, these methods are grouped in filters, wrappers, and embedded methods. More recently, a new group of

methods has been added in the general framework of FS: ensemble techniques. The focus in this survey is on filter

feature selection methods for informative feature discovery in gene expression microarray (GEM) analysis, which is also

known as differentially expressed genes (DEGs) discovery, gene prioritization, or biomarker discovery. We present them

in a unified framework, using standardized notations in order to reveal their technical details and to highlight their

common characteristics as well as their particularities.

In this paper, we investigate the problem of reverse engineering the topology of gene regulatory networks from temporal

gene expression data. We adopt a computational intelligence approach comprising swarm intelligence techniques,

namely particle swarm optimization (PSO) and ant colony optimization (ACO). In addition, the recurrent neural network

A Sparse Regulatory Network of Copy-Number Driven Gene Expression Reveals

Putative Breast Cancer Oncogenes

A Swarm Intelligence Framework for Reconstructing Gene Networks: Searching for

Biologically Plausible Architectures

A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray

Analysis



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(RNN) formalism is employed for modeling the dynamical behavior of gene regulatory systems. More specifically, ACO is

used for searching the discrete space of network architectures and PSO for searching the corresponding continuous

space of RNN model parameters. We propose a novel solution construction process in the context of ACO for

generating biologically plausible candidate architectures. The objective is to concentrate the search effort into areas of

the structure space that contain architectures which are feasible in terms of their topological resemblance to real-world

networks. The proposed framework is initially applied to the reconstruction of a small artificial network that has

previously been studied in the context of gene network reverse engineering. Subsequently, we consider an artificial data

set with added noise for reconstructing a subnetwork of the genetic interaction network of S. cerevisiae (yeast). Finally,

the framework is applied to a real-world data set for reverse engineering the SOS response system of the bacterium

Escherichia coli. Results demonstrate the relative advantage of utilizing problem-specific knowledge regarding

biologically plausible structural properties of gene networks over conducting a problem-agnostic search in the vast

space of network architectures.

Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could

perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection.

Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample

classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly

of size h), then selects informative smaller subsets of genes (of size r <; h) from a subset and merges the chosen genes

with another gene subset (of size r) to update the gene subset. We repeat this process until all subsets are merged into

one informative subset. We illustrate the effectiveness of the proposed algorithm by analyzing three distinct gene

expression data sets. Our method shows promising classification accuracy for all the test data sets. We also show the

relevance of the selected genes in terms of their biological functions.

A reticulate network N of multiple phylogenetic trees may have nodes with two or more parents (called reticulation

nodes). There are two ways to define the reticulation number of N. One way is to define it as the number of reticulation

nodes in N in this case, a reticulate network with the smallest reticulation number is called an optimal type-I reticulate

network of the trees. The better way is to define it as the total number of parents of reticulation nodes in N minus the

number of reticulation nodes in N ; in this case, a reticulate network with the smallest reticulation number is called an

optimal type-II reticulate network of the trees. In this paper, we first present a fast fixed-parameter algorithm for

constructing one or all optimal type-I reticulate networks of multiple phylogenetic trees. We then use the algorithm

together with other ideas to obtain an algorithm for estimating a lower bound on the reticulation number of an optimal

type-II reticulate network of the input trees. To our knowledge, these are the first fixed-parameter algorithms for the

A Top-r Feature Selection Algorithm for Microarray Gene Expression Data

Algorithms for Reticulate Networks of Multiple Phylogenetic Trees



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problems. We have implemented the algorithms in ANSI C, obtaining programs CMPT and MaafB. Our experimental data

show that CMPT can construct optimal type-I reticulate networks rapidly and MaafB can compute better lower bounds

for optimal type-II reticulate networks within shorter time than the previously best program PIRN designed by Wu.

Detecting essential multiprotein modules that change infrequently during evolution is a challenging algorithmic task that

is important for understanding the structure, function, and evolution of the biological cell. In this paper, we define a

measure of modularity for interactomes and present a linear-time algorithm, Produles, for detecting multiprotein

modularity conserved during evolution that improves on the running time of previous algorithms for related problems

and offers desirable theoretical guarantees. We present a biologically motivated graph theoretic set of evaluation

measures complementary to previous evaluation measures, demonstrate that Produles exhibits good performance by all

measures, and describe certain recurrent anomalies in the performance of previous algorithms that are not detected by

previous measures. Consideration of the newly defined measures and algorithm performance on these measures leads

to useful insights on the nature of interactomics data and the goals of previous and current algorithms. Through

randomization experiments, we demonstrate that conserved modularity is a defining characteristic of interactomes.

Computational experiments on current experimentally derived interactomes for Homo sapiens and Drosophila

melanogaster, combining results across algorithms, show that nearly 10 percent of current interactome proteins

participate in multiprotein modules with good evidence in the protein interaction data of being conserved between

human and Drosophila.

Haplotype Inference (HI) is a computational challenge of crucial importance in a range of genetic studies. Pedigrees

allow to infer haplotypes from genotypes more accurately than population data, since Mendelian inheritance restricts the

set of possible solutions. In this work, we define a new HI problem on pedigrees, called Minimum-Change Haplotype

Configuration (MCHC) problem, that allows two types of genetic variation events: recombinations and mutations. Our

new formulation extends the Minimum-Recombinant Haplotype Configuration (MRHC) problem, that has been proposed

in the literature to overcome the limitations of classic statistical haplotyping methods. Our contribution is twofold. First,

we prove that the MCHC problem is APX-hard under several restrictions. Second, we propose an efficient and accurate

heuristic algorithm for MCHC based on an L-reduction to a well-known coding problem. Our heuristic can also be used

to solve the original MRHC problem and can take advantage of additional knowledge about the input genotypes.

Moreover, the L-reduction proves for the first time that MCHC and MRHC are O(nm/log nm)-approximable on general

pedigrees, where n is the pedigree size and m is the genotype length. Finally, we present an extensive experimental

evaluation and comparison of our heuristic algorithm with several other state-of-the-art methods for HI on pedigrees.

Algorithms to Detect Multi-protein Modularity Conserved during Evolution

An Efficient Algorithm for Haplotype Inferenceon Pedigrees with Recombinations and

Mutations



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Background. Inferring an evolutionary scenario for a gene family is a fundamental problem with applications both in

functional and evolutionary genomics. The gene tree/species tree reconciliation approach has been widely used to

address this problem, but mostly in a discrete parsimony framework that aims at minimizing the number of gene

duplications and/or gene losses. Recently, a probabilistic approach has been developed, based on the classical birth-

and-death process, including efficient algorithms for computing posterior probabilities of reconciliations and orthology

prediction. Results. In previous work, we described an algorithm for exploring the whole space of gene tree/species tree

reconciliations, that we adapt here to compute efficiently the posterior probability of such reconciliations. These

posterior probabilities can be either computed exactly or approximated, depending on the reconciliation space size. We

use this algorithm to analyze the probabilistic landscape of the space of reconciliations for a real data set of fungal gene

families and several data sets of synthetic gene trees. Conclusion. The results of our simulations suggest that, with

exact gene trees obtained by a simple birth-and-death process and realistic gene duplication/loss rates, a very small

subset of all reconciliations needs to be explored in order to approximate very closely the posterior probability of the

most likely reconciliations. For cases where the posterior probability mass is more evenly dispersed, our method allows

to explore efficiently the required subspace of reconciliations.

Characterization of the kinetic and conformational properties of channel proteins is a crucial element in the integrative

study of congenital cardiac diseases. The proteins of the ion channels of cardiomyocytes represent an important family

of biological components determining the physiology of the heart. Some computational studies aiming to understand

the mechanisms of the ion channels of cardiomyocytes have concentrated on Markovian stochastic approaches.

Mathematically, these approaches employ Chapman-Kolmogorov equations coupled with partial differential equations.

As the scale and complexity of such subcellular and cellular models increases, the balance between efficiency and

accuracy of algorithms becomes critical. We have developed a novel two-stage splitting algorithm to address efficiency

and accuracy issues arising in such modeling and simulation scenarios. Numerical experiments were performed based

on the incorporation of our newly developed conformational kinetic model for the rapid delayed rectifier potassium

channel into the dynamic models of human ventricular myocytes. Our results show that the new algorithm significantly

outperforms commonly adopted adaptive Runge-Kutta methods. Furthermore, our parallel simulations with coupled

algorithms for multicellular cardiac tissue demonstrate a high linearity in the speedup of large-scale cardiac simulations.

An Efficient Method for Exploring the Space of Gene Tree/Species Tree Reconciliations in a

Probabilistic Framework

An Efficient Method for Modeling Kinetic Behavior of Channel Proteins in

Cardiomyocytes

Cluster-Oriented Ensemble Classifier: Impact of Multicluster Characterization on

Ensemble Classifier Learning



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All clustering methods have to assume some cluster relationship among the data objects that they are applied on.

Similarity between a pair of objects can be defined either explicitly or implicitly. In this paper, we introduce a novel

multiviewpoint-based similarity measure and two related clustering methods. The major difference between a traditional

dissimilarity/similarity measure and ours is that the former uses only a single viewpoint, which is the origin, while the

latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects

being measured. Using multiple viewpoints, more informative assessment of similarity could be achieved. Theoretical

analysis and empirical study are conducted to support this claim. Two criterion functions for document clustering are

proposed based on this new measure. We compare them with several well-known clustering algorithms that use other

popular similarity measures on various document collections to verify the advantages of our proposal.

We introduce a class of finite systems models of gene regulatory networks exhibiting behavior of the cell cycle. The

network is an extension of a Boolean network model. The system spontaneously cycles through a finite set of internal

states, tracking the increase of an external factor such as cell mass, and also exhibits checkpoints in which errors in

gene expression levels due to cellular noise are automatically corrected. We present a 7-gene network based on

Projective Geometry codes, which can correct, at every given time, one gene expression error. The topology of a

network is highly symmetric and requires using only simple Boolean functions that can be synthesized using genes of

various organisms. The attractor structure of the Boolean network contains a single cycle attractor. It is the smallest

nontrivial network with such high robustness. The methodology allows construction of artificial gene regulatory

networks with the number of phases larger than in natural cell cycle.

Peptide sequencing from mass spectrometry data is a key step in proteome research. Especially de novo sequencing,

the identification of a peptide from its spectrum alone, is still a challenge even for state-of-the-art algorithmic

approaches. In this paper, we present antilope, a new fast and flexible approach based on mathematical programming. It

builds on the spectrum graph model and works with a variety of scoring schemes. ANTILOPE combines Lagrangian

relaxation for solving an integer linear programming formulation with an adaptation of Yen's k shortest paths algorithm.

It shows a significant improvement in running time compared to mixed integer optimization and performs at the same

speed like other state-of-the-art tools. We also implemented a generic probabilistic scoring scheme that can be trained

automatically for a data set of annotated spectra and is independent of the mass spectrometer type. Evaluations on

benchmark data show that antilope is competitive to the popular state-of-the-art programs PepNovo and NovoHMM both

in terms of runtime and accuracy. Furthermore, it offers increased flexibility in the number of considered ion types.

ANTILOPE will be freely available as part of the open source proteomics library OpenMS.

An Information Theoretic Approach to Constructing Robust Boolean Gene Regulatory

Networks

Assortative Mixing in Directed Biological Networks

Antilope—A Lagrangian Relaxation Approach to the de novo Peptide Sequencing

Problem



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We analyze assortative mixing patterns of biological networks which are typically directed. We develop a theoretical

background for analyzing mixing patterns in directed networks before applying them to specific biological networks.

Two new quantities are introduced, namely the in-assortativity and the out-assortativity, which are shown to be useful in

quantifying assortative mixing in directed networks. We also introduce the local (node level) assortativity quantities for

in- and out-assortativity. Local assortativity profiles are the distributions of these local quantities over node degrees and

can be used to analyze both canonical and real-world directed biological networks. Many biological networks, which

have been previously classified as disassortative, are shown to be assortative with respect to these new measures.

Finally, we demonstrate the use of local assortativity profiles in analyzing the functionalities of particular nodes and

groups of nodes in real-world biological networks.

Here, we propose BpMatch: an algorithm that, working on a suitably modified suffix-tree data structure, is able to

compute, in a fast and efficient way, the coverage of a source sequence S on a target sequence T, by taking into account

direct and reverse segments, eventually overlapped. Using BpMatch, the operator should define a priori, the minimum

length l of a segment and the minimum number of occurrences minRep, so that only segments longer than l and having

a number of occurrences greater than minRep are considered to be significant. BpMatch outputs the significant

segments found and the computed segment-based distance. On the worst case, assuming the alphabet dimension d is a

constant, the time required by BpMatch to calculate the coverage is {rm O}(l^2n). On the average, by setting lge

2log_d(n), the time required to calculate the coverage is only {rm O}(n). BpMatch, thanks to the minRep parameter, can

also be used to perform a self-covering: to cover a sequence using segments coming from itself, by avoiding the trivial

solution of having a single segment coincident with the whole sequence.

Ab initio protein structure prediction methods first generate large sets of structural conformations as candidates (called

decoys), and then select the most representative decoys through clustering techniques. Classical clustering methods

are inefficient due to the pairwise distance calculation, and thus become infeasible when the number of decoys is large.

In addition, the existing clustering approaches suffer from the arbitrariness in determining a distance threshold for

proteins within a cluster: a small distance threshold leads to many small clusters, while a large distance threshold

results in the merging of several independent clusters into one cluster. In this paper, we propose an efficient clustering

method through fast estimating cluster centroids and efficient pruning rotation spaces. The number of clusters is

automatically detected by information distance criteria. A package named ONION, which can be downloaded freely, is

implemented accordingly. Experimental results on benchmark data sets suggest that ONION is 14 times faster than

BpMatch: An Efficient Algorithm for a Segmental Analysis of Genomic Sequences

Clustering 100,000 Protein Structure Decoysin Minutes



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existing tools, and ONION obtains better selections for 31 targets, and worse selection for 19 targets compared to

SPICKER's selections. On an average PC, ONION can cluster 100,000 decoys in around 12 minutes.

The composition vector (CV) method is an alignment-free method for sequence comparison. Because of its simplicity

when compared with multiple sequence alignment methods, the method has been widely discussed lately; and some

formulas based on probabilistic models, like Hao's and Yu's formulas, have been proposed. In this paper, we improve

these formulas by using the entropy principle which can quantify the nonrandomness occurrence of patterns in the

sequences. More precisely, existing formulas are used to generate a set of possible formulas from which we choose the

one that maximizes the entropy. We give the closed-form solution to the resulting optimization problem. Hence, from any

given CV formula, we can find the corresponding one that maximizes the entropy. In particular, we show that Hao's

formula is itself maximizing the entropy and we derive a new entropy-maximizing formula from Yu's formula. We

illustrate the accuracy of our new formula by using both simulated and experimental data sets. For the simulated data

sets, our new formula gives the best consensus and significant values for three different kinds of evolution models. For

the data set of tetrapod 18S rRNA sequences, our new formula groups the clades of bird and reptile together correctly,

where Hao's and Yu's formulas failed. Using real data sets with different sizes, we show that our formula is more

accurate than Hao's and Yu's formulas even for small data sets.

Split networks are commonly used to visualize collections of bipartitions, also called splits, of a finite set. Such

collections arise, for example, in evolutionary studies. Split networks can be viewed as a generalization of phylogenetic

trees and may be generated using the SplitsTree package. Recently, the NeighborNet method for generating split

networks has become rather popular, in part because it is guaranteed to always generate a circular split system, which

can always be displayed by a planar split network. Even so, labels must be placed on the "outside” of the network,

which might be problematic in some applications. To help circumvent this problem, it can be helpful to consider so-

called flat split systems, which can be displayed by planar split networks where labels are allowed on the inside of the

network too. Here, we present a new algorithm that is guaranteed to compute a minimal planar split network displaying a

flat split system in polynomial time, provided the split system is given in a certain format. We will also briefly discuss

two heuristics that could be useful for analyzing phylogeographic data and that allow the computation of flat split

systems in this format in polynomial time.

Composition Vector Method Based on Maximum Entropy Principle for Sequence

Comparison

Constructing and Drawing Regular Planar Split Networks

Constructing Complex 3D Biological Environments from Medical Imaging Using High

Performance Computing



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Extracting information about the structure of biological tissue from static image data is a complex task requiring

computationally intensive operations. Here, we present how multicore CPUs and GPUs have been utilized to extract

information about the shape, size, and path followed by the mammalian oviduct, called the fallopian tube in humans,

from histology images, to create a unique but realistic 3D virtual organ. Histology images were processed to identify the

individual cross sections and determine the 3D path that the tube follows through the tissue. This information was then

related back to the histology images, linking the 2D cross sections with their corresponding 3D position along the

oviduct. A series of linear 2D spline cross sections, which were computationally generated for the length of the oviduct,

were bound to the 3D path of the tube using a novel particle system technique that provides smooth resolution of self-

intersections. This results in a unique 3D model of the oviduct, which is grounded in reality. The GPU is used for the

processor intensive operations of image processing and particle physics based simulations, significantly reducing the

time required to generate a complete model.

Since Darwin, species trees have been used as a simplified description of the relationships which summarize the

complicated network N of reality. Recent evidence of hybridization and lateral gene transfer, however, suggest that there

are situations where trees are inadequate. Consequently it is important to determine properties that characterize

networks closely related to N and possibly more complicated than trees but lacking the full complexity of N. A

connected surjective digraph map (CSD) is a map f from one network N to another network M such that every arc is

either collapsed to a single vertex or is taken to an arc, such that f is surjective, and such that the inverse image of a

vertex is always connected. CSD maps are shown to behave well under composition. It is proved that if there is a CSD

map from N to M, then there is a way to lift an undirected version of M into N, often with added resolution. A CSD map

from N to M puts strong constraints on N. In general, it may be useful to study classes of networks such that, for any N,

there exists a CSD map from N to some standard member of that class.

Detecting members of known noncoding RNA (ncRNA) families in genomic DNA is an important part of sequence

annotation. However, the most widely used tool for modeling ncRNA families, the covariance model (CM), incurs a high-

computational cost when used for genome-wide search. This cost can be reduced by using a filter to exclude sequences

that are unlikely to contain the ncRNA of interest, applying the CM only where it is likely to match strongly. Despite

recent advances, designing an efficient filter that can detect ncRNA instances lacking strong conservation while

excluding most irrelevant sequences remains challenging. In this work, we design three types of filters based on

multiple secondary structure profiles (SSPs). An SSP augments a regular profile (i.e., a position weight matrix) with

secondary structure information but can still be efficiently scanned against long sequences. Multi-SSP-based filters

combine evidence from multiple SSP matches and can achieve high sensitivity and specificity. Our SSP-based filters are

extensively tested in BRAliBase III data set, Rfam 9.0, and a published soil metagenomic data set. In addition, we

CSD Homomorphisms between Phylogenetic Networks

Designing Filters for Fast-Known NcRNA Identification



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compare the SSP-based filters with several other ncRNA search tools including Infernal (with profile HMMs as filters),

ERPIN, and tRNAscan-SE. Our experiments demonstrate that carefully designed SSP filters can achieve significant

speedup over unfiltered CM search while maintaining high sensitivity for various ncRNA families.

Unlike Sequence-based understanding and identification of protein binding interfaces is a challenging research topic

due to the complexity in protein systems and the imbalanced distribution between interface and noninterface residues.

This paper presents an outlier detection idea to address the redundancy problem in protein interaction data. The cleaned

training data are then used for improving the prediction performance. We use three novel measures to describe the

extent a residue is considered as an outlier in comparison to the other residues: the distance of a residue instance from

the center instance of all residue instances of the same class label (Dist), the probability of the class label of the residue

instance (PCL), and the importance of within-class and between-class (IWB) residue instances. Outlier scores are

computed by integrating the three factors; instances with a sufficiently large score are treated as outliers and removed.

The data sets without outliers are taken as input for a support vector machine (SVM) ensemble. The proposed SVM

ensemble trained on input data without outliers performs better than that with outliers. Our method is also more

accurate than many literature methods on benchmark data sets. From our empirical studies, we found that some outlier

interface residues are truly near to noninterface regions, and some outlier noninterface residues are close to interface

regions.

Clustering has a long and rich history in a variety of scientific fields. Finding natural groupings of a data set is a hard

task as attested by hundreds of clustering algorithms in the literature. Each clustering technique makes some

assumptions about the underlying data set. If the assumptions hold, good clusterings can be expected. It is hard, in

some cases impossible, to satisfy all the assumptions. Therefore, it is beneficial to apply different clustering methods on

the same data set, or the same method with varying input parameters or both. We propose a novel method, DICLENS,

which combines a set of clusterings into a final clustering having better overall quality. Our method produces the final

clustering automatically and does not take any input parameters, a feature missing in many existing algorithms.

Extensive experimental studies on real, artificial, and gene expression data sets demonstrate that DICLENS produces

very good quality clusterings in a short amount of time. DICLENS implementation runs on standard personal computers

by being scalable, and by consuming very little memory and CPU.

Detection of Outlier Residues for Improving Interface Prediction in Protein Heterocomplexes

DICLENS: Divisive Clustering Ensemble with Automatic Cluster Number

Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a

Reject Option



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Many Genome-wide association studies (GWA) try to identify the genetic polymorphisms associated with variation in

phenotypes. However, the most significant genetic variants may have a small predictive power to forecast the future

development of common diseases. We study the prediction of the risk of developing a disease given genome-wide

genotypic data using classifiers with a reject option, which only make a prediction when they are sufficiently certain, but

in doubtful situations may reject making a classification. To test the reliability of our proposal, we used the Wellcome

Trust Case Control Consortium (WTCCC) data set, comprising 14,000 cases of seven common human diseases and

3,000 shared controls.

In the studies of Drosophila embryogenesis, a large number of two-dimensional digital images of gene expression

patterns have been produced to build an atlas of spatio-temporal gene expression dynamics across developmental time.

Gene expressions captured in these images have been manually annotated with anatomical and developmental ontology

terms using a controlled vocabulary (CV), which are useful in research aimed at understanding gene functions,

interactions, and networks. With the rapid accumulation of images, the process of manual annotation has become

increasingly cumbersome, and computational methods to automate this task are urgently needed. However, the

automated annotation of embryo images is challenging. This is because the annotation terms spatially correspond to

local expression patterns of images, yet they are assigned collectively to groups of images and it is unknown which

term corresponds to which region of which image in the group. In this paper, we address this problem using a new

machine learning framework, Multi-Instance Multi-Label (MIML) learning. We first show that the underlying nature of the

annotation task is a typical MIML learning problem. Then, we propose two support vector machine algorithms under the

MIML framework for the task. Experimental results on the FlyExpress database (a digital library of standardized

Drosophila gene expression pattern images) reveal that the exploitation of MIML framework leads to significant

performance improvement over state-of-the-art approaches.

The 3D conformation of a protein in the space is the main factor which determines its function in living organisms. Due

to the huge amount of newly discovered proteins, there is a need for fast and accurate computational methods for

retrieving protein structures. Their purpose is to speed up the process of understanding the structure-to-function

relationship which is crucial in the development of new drugs. There are many algorithms addressing the problem of

protein structure retrieval. In this paper, we present several novel approaches for retrieving protein tertiary structures.

We present our voxel-based descriptor. Then we present our protein ray-based descriptors which are applied on the

interpolated protein backbone. We introduce five novel wavelet descriptors which perform wavelet transforms on the

protein distance matrix. We also propose an efficient algorithm for distance matrix alignment named Matrix Alignment

by Sequence Alignment within Sliding Window (MASASW), which has shown as much faster than DALI, CE, and

Drosophila Gene Expression Pattern Annotation through Multi-Instance Multi-Label

Learning

Efficient Approaches for Retrieving Protein Tertiary Structures



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MatAlign. We compared our approaches between themselves and with several existing algorithms, and they generally

prove to be fast and accurate. MASASW achieves the highest accuracy. The ray and wavelet-based descriptors as well

as MASASW are more accurate than CE.

In We propose the technique of Adaptive Allele Consolidation, that greatly improves the performance of the Lange-

Goradia algorithm for genotype elimination in pedigrees, while still producing equivalent output. Genotype elimination

consists in removing from a pedigree those genotypes that are impossible according to the Mendelian law of

inheritance. This is used to find errors in genetic data and is useful as a preprocessing step in other analyses (such as

linkage analysis or haplotype imputation). The problem of genotype elimination is intrinsically combinatorial, and Allele

Consolidation is an existing technique where several alleles are replaced by a single "lumped” allele in order to reduce

the number of combinations of genotypes that have to be considered, possibly at the expense of precision. In existing

Allele Consolidation techniques, alleles are lumped once and for all before performing genotype elimination. The idea of

Adaptive Allele Consolidation is to dynamically change the set of alleles that are lumped together during the execution

of the Lange-Goradia algorithm, so that both high performance and precision are achieved. We have implemented the

technique in a tool called Celer and evaluated it on a large set of scenarios, with good results.

Finding repetitive structures in genomes and proteins is important to understand their biological functions. Many data

compressors for modern genomic sequences rely heavily on finding repeats in the sequences. Small-scale and local

repetitive structures are better understood than large and complex interspersed ones. The notion of maximal repeats

captures all the repeats in the data in a space-efficient way. Prior work on maximal repeat finding used either a suffix

tree or a suffix array along with other auxiliary data structures. Their space usage is 19-50 times the text size with the

best engineering efforts, prohibiting their usability on massive data such as the whole human genome. We focus on

finding all the maximal repeats from massive texts in a time- and space-efficient manner. Our technique uses the

Burrows-Wheeler Transform and wavelet trees. For data sets consisting of natural language texts and protein data, the

space usage of our method is no more than three times the text size. For genomic sequences stored using one byte per

base, the space usage of our method is less than double the sequence size. Our space-efficient method keeps the

timing performance fast. In fact, our method is orders of magnitude faster than the prior methods for processing

massive texts such as the whole human genome, since the prior methods must use external memory. For the first time,

our method enables a desktop computer with 8 GB internal memory (actual internal memory usage is less than 6 GB) to

find all the maximal repeats in the whole human genome in less than 17 hours. We have implemented our method as

general-purpose open-source software for public use.

Efficient Genotype Eliminationvia Adaptive Allele Consolidation

Efficient Maximal Repeat Finding Using the Burrows-Wheeler Transform and Wavelet Tree



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The High-throughput methods systematically measure the internal state of the entire cell, but powerful computational

tools are needed to infer dynamics from their raw data. Therefore, we have developed a new computational method,

Eigen-genomic System Dynamic-pattern Analysis (ESDA), which uses systems theory to infer dynamic parameters from

a time series of gene expression measurements. As many genes are measured at a modest number of time points,

estimation of the system matrix is underdetermined and traditional approaches for estimating dynamic parameters are

ineffective; thus, ESDA uses the principle of dimensionality reduction to overcome the data imbalance. Since

degradation rates are naturally confounded by self-regulation, our model estimates an effective degradation rate that is

the difference between self-regulation and degradation. We demonstrate that ESDA is able to recover effective

degradation rates with reasonable accuracy in simulation. We also apply ESDA to a budding yeast data set, and find that

effective degradation rates are normally slower than experimentally measured degradation rates. Our results suggest

that either self-regulation is widespread in budding yeast and that self-promotion dominates self-inhibition, or that self-

regulation may be rare and that experimental methods for measuring degradation rates based on transcription arrest

may severely overestimate true degradation rates in healthy cells.

.

The availability of a great range of prior biological knowledge about the roles and functions of genes and gene-gene

interactions allows us to simplify the analysis of gene expression data to make it more robust, compact, and

interpretable. Here, we objectively analyze the applicability of functional clustering for the identification of groups of

functionally related genes. The analysis is performed in terms of gene expression classification and uses predictive

accuracy as an unbiased performance measure. Features of biological samples that originally corresponded to genes

are replaced by features that correspond to the centroids of the gene clusters and are then used for classifier learning.

Using 10 benchmark data sets, we demonstrate that functional clustering significantly outperforms random clustering

without biological relevance. We also show that functional clustering performs comparably to gene expression

clustering, which groups genes according to the similarity of their expression profiles. Finally, the suitability of

functional clustering as a feature extraction technique is evaluated and discussed.

This The recent advances in the infrastructure of Geographic Information Systems (GIS), and the proliferation of GPS

technology, have resulted in the abundance of geodata in the form of sequences of points of interest (POIs), waypoints,

etc. We refer to sets of such sequences as route collections. In this work, we consider path queries on frequently

updated route collections: given a route collection and two points n_s and n_t, a path query returns a path, i.e., a

sequence of points, that connects n_s to n_t. We introduce two path query evaluation paradigms that enjoy the benefits

of search algorithms (i.e., fast index maintenance) while utilizing transitivity information to terminate the search sooner.

Eigen-Genomic System Dynamic-Pattern Analysis (ESDA): Modeling mRNA Degradation

and Self-Regulation

Empirical Evidence of the Applicability of Functional Clustering through Gene Expression

Classification

Evaluating Path Queries over Frequently Updated Route Collections



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Efficient indexing schemes and appropriate updating procedures are introduced. An extensive experimental evaluation

verifies the advantages of our methods compared to conventional graph-based search.

.

Predicting the secondary structure of proteins is still a typical step in several bioinformatic tasks, in particular, for

tertiary structure prediction. Notwithstanding the impressive results obtained so far, mostly due to the advent of

sequence encoding schemes based on multiple alignment, in our view the problem should be studied from a novel

perspective, in which understanding how available information sources are dealt with plays a central role. After

revisiting a well-known secondary structure predictor viewed from this perspective (with the goal of identifying which

sources of information have been considered and which have not), we propose a generic software architecture designed

to account for all relevant information sources. To demonstrate the validity of the approach, a predictor compliant with

the proposed generic architecture has been implemented and compared with several state-of-the-art secondary

structure predictors. Experiments have been carried out on standard data sets, and the corresponding results confirm

the validity of the approach. The predictor is available at http://iasc.diee.unica.it/ssp2/ through the corresponding web

application or as downloadable stand-alone portable unpack-and-run bundle.

This Predicting the secondary structure of proteins is still a typical step in several bioinformatic tasks, in particular, for

tertiary structure prediction. Notwithstanding the impressive results obtained so far, mostly due to the advent of

sequence encoding schemes based on multiple alignment, in our view the problem should be studied from a novel

perspective, in which understanding how available information sources are dealt with plays a central role. After

revisiting a well-known secondary structure predictor viewed from this perspective (with the goal of identifying which

sources of information have been considered and which have not), we propose a generic software architecture designed

to account for all relevant information sources. To demonstrate the validity of the approach, a predictor compliant with

the proposed generic architecture has been implemented and compared with several state-of-the-art secondary

structure predictors. Experiments have been carried out on standard data sets, and the corresponding results confirm

the validity of the approach.

A Robinson-Foulds (RF) supertree for a collection of input trees is a tree containing all the species in the input trees that

is at minimum total RF distance to the input trees. Thus, an RF supertree is consistent with the maximum number of

splits in the input trees. Constructing RF supertrees for rooted and unrooted data is NP-hard. Nevertheless, effective

local search heuristics have been developed for the restricted case where the input trees and the supertree are rooted.

We describe new heuristics, based on the Edge Contract and Refine (ECR) operation, that remove this restriction,

Exploiting Intrastructure Information for Secondary Structure Prediction with Multifaceted

Pipelines

Exploiting the Functional and Taxonomic Structure of Genomic Data by Probabilistic

Topic Modeling

Fast Local Search for Unrooted Robinson-Foulds Supertrees



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thereby expanding the utility of RF supertrees. Our experimental results on simulated and empirical data sets show that

our unrooted local search algorithms yield better supertrees than those obtained from MRP and rooted RF heuristics in

terms of total RF distance to the input trees and, for simulated data, in terms of RF distance to the true tree.

Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks.

However, with increasing vast amount of data on biological networks, performance and scalability issues are becoming

a critical limiting factor in applications. Meanwhile, GPU computing, which uses CUDA tool for implementing a massively

parallel computing environment in the GPU card, is becoming a very powerful, efficient, and low-cost option to achieve

substantial performance gains over CPU approaches. The use of on-chip memory on the GPU is efficiently lowering the

latency time, thus, circumventing a major issue in other parallel computing environments, such as MPI. We introduce a

very fast Markov clustering algorithm using CUDA (CUDA-MCL) to perform parallel sparse matrix-matrix computations

and parallel sparse Markov matrix normalizations, which are at the heart of MCL. We utilized ELLPACK-R sparse format

to allow the effective and fine-grain massively parallel processing to cope with the sparse nature of interaction networks

data sets in bioinformatics applications. As the results show, CUDA-MCL is significantly faster than the original MCL

running on CPU. Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only

possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with

their data.

The problem of identifying the proteins in a complex mixture using tandem mass spectrometry can be framed as an

inference problem on a graph that connects peptides to proteins. Several existing protein identification methods make

use of statistical inference methods for graphical models, including expectation maximization, Markov chain Monte

Carlo, and full marginalization coupled with approximation heuristics. We show that, for this problem, the majority of the

cost of inference usually comes from a few highly connected subgraphs. Furthermore, we evaluate three different

statistical inference methods using a common graphical model, and we demonstrate that junction tree inference

substantially improves rates of convergence compared to existing methods.

The problem of identifying the proteins in a complex mixture using tandem mass spectrometry can be framed as an

inference problem on a graph that connects peptides to proteins. Several existing protein identification methods make

use of statistical inference methods for graphical models, including expectation maximization, Markov chain Monte

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Computing on

GPU with CUDA and ELLPACK-R Sparse Format

Faster Mass Spectrometry-Based Protein Inference: Junction Trees Are More Efficient

than Sampling and Marginalization by Enumeration

Gene Classification Using Parameter-Free Semi-Supervised Manifold Learning



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Carlo, and full marginalization coupled with approximation heuristics. We show that, for this problem, the majority of the

cost of inference usually comes from a few highly connected subgraphs. Furthermore, we evaluate three different

statistical inference methods using a common graphical model, and we demonstrate that junction tree inference

substantially improves rates of convergence compared to existing methods.

Reconstruction of signaling pathway structures is essential to decipher complex regulatory relationships in living cells.

The existing computational approaches often rely on unrealistic biological assumptions and do not explicitly consider

signal transduction mechanisms. Signal transduction events refer to linear cascades of reactions from the cell surface

to the nucleus and characterize a signaling pathway. In this paper, we propose a novel approach, Gene Set Gibbs

Sampling (GSGS), to reverse engineer signaling pathway structures from gene sets related to the pathways. We

hypothesize that signaling pathways are structurally an ensemble of overlapping linear signal transduction events which

we encode as Information Flows (IFs). We infer signaling pathway structures from gene sets, referred to as Information

Flow Gene Sets (IFGSs), corresponding to these events. Thus, an IFGS only reflects which genes appear in the

underlying IF but not their ordering. GSGS offers a Gibbs sampling like procedure to reconstruct the underlying

signaling pathway structure by sequentially inferring IFs from the overlapping IFGSs related to the pathway. In the proof-

of-concept studies, our approach is shown to outperform the existing state-of-the-art network inference approaches

using both continuous and discrete data generated from benchmark networks in the DREAM initiative. We perform a

comprehensive sensitivity analysis to assess the robustness of our approach. Finally, we implement GSGS to

reconstruct signaling mechanisms in breast cancer cells.

Extracting protein-protein interaction (PPI) from biomedical literature is an important task in biomedical text mining

(BioTM). In this paper, we propose a hash subgraph pairwise (HSP) kernel-based approach for this task. The key to the

novel kernel is to use the hierarchical hash labels to express the structural information of subgraphs in a linear time. We

apply the graph kernel to compute dependency graphs representing the sentence structure for protein-protein

interaction extraction task, which can efficiently make use of full graph structural information, and particularly capture

the contiguous topological and label information ignored before. We evaluate the proposed approach on five publicly

available PPI corpora. The experimental results show that our approach significantly outperforms all-path kernel

approach on all five corpora and achieves state-of-the-art performance.

GSGS: A Computational Approach to Reconstruct Signaling Pathway Structures from

Gene Sets

Identification of Essential Proteins Based on Edge Clustering Coefficient

Hash Subgraph Pairwise Kernel for Protein-Protein Interaction Extraction



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Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for

drug design. The rapid increase of available protein-protein interaction (PPI) data has made it possible to detect protein

essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based

on network topology. However, most of them tended to focus only on the location of single protein, but ignored the

relevance between interactions and protein essentiality. In this paper, a new centrality measure for identifying essential

proteins based on edge clustering coefficient, named as NC, is proposed. Different from previous centrality measures,

NC considers both the centrality of a node and the relationship between it and its neighbors. For each interaction in the

network, we calculate its edge clustering coefficient. A node's essentiality is determined by the sum of the edge

clustering coefficients of interactions connecting it and its neighbors. The new centrality measure NC takes into account

the modular nature of protein essentiality. NC is applied to three different types of yeast protein-protein interaction

networks, which are obtained from the DIP database, the MIPS database and the BioGRID database, respectively. The

experimental results on the three different networks show that the number of essential proteins discovered by NC

universally exceeds that discovered by the six other centrality measures: DC, BC, CC, SC, EC, and IC. Moreover, the

essential proteins discovered by NC show significant cluster effect.

Information We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To

incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene

expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic

regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer

phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting

tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks

based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the

cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene

expression data.

This paper considers the problem of learning the structure of gene regulatory networks from gene expression time

series data. A more realistic scenario when the state space model representing a gene network evolves nonlinearly is

considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter-based

state estimation algorithm is considered instead of the contemporary linear approximation-based approaches. The

parameters characterizing the regulatory relations among various genes are estimated online using a Kalman filter.

Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state

estimates delivered by the particle filter and the observed microarray data are then subjected to a LASSO-based least

Identifying Gene Pathways Associated with Cancer Characteristics via Sparse Statistical

Methods

Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting

Sparsity



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squares regression operation which yields a parsimonious and efficient description of the regulatory network by setting

the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with the extended

Kalman filter (EKF) and Unscented Kalman Filter (UKF) employing the Mean Square Error (MSE) as the fidelity criterion

in recovering the parameters of gene regulatory networks from synthetic data and real biological data. Extensive

computer simulations illustrate that the proposed particle filter-based network inference algorithm outperforms EKF and

UKF, and therefore, it can serve as a natural framework for modeling gene regulatory networks with nonlinear and

sparse structure.

Forensic samples containing DNA from two or more individuals can be difficult to interpret. Even ascertaining the

number of contributors to the sample can be challenging. These uncertainties can dramatically reduce the statistical

weight attached to evidentiary samples. A probabilistic mixture algorithm that takes into account not just the number

and magnitude of the alleles at a locus, but also their frequency of occurrence allows the determination of likelihood

ratios of different hypotheses concerning the number of contributors to a specific mixture. This probabilistic mixture

algorithm can compute the probability of the alleles in a sample being present in a 2-person mixture, 3-person mixture,

etc. The ratio of any two of these probabilities then constitutes a likelihood ratio pertaining to the number of contributors

to such a mixture.

A salient purpose for studying gene regulatory networks is to derive intervention strategies to identify potential drug

targets and design gene-based therapeutic intervention. Optimal and approximate intervention strategies based on the

transition probability matrix of the underlying Markov chain have been studied extensively for probabilistic Boolean

networks. While the key goal of control is to reduce the steady-state probability mass of undesirable network states, in

practice it is important to limit collateral damage and this constraint should be taken into account when designing

intervention strategies with network models. In this paper, we propose two new phenotypically constrained stationary

control policies by directly investigating the effects on the network long-run behavior. They are derived to reduce the

risk of visiting undesirable states in conjunction with constraints on the shift of undesirable steady-state mass so that

only limited collateral damage can be introduced. We have studied the performance of the new constrained control

policies together with the previous greedy control policies to randomly generated probabilistic Boolean networks. A

preliminary example for intervening in a metastatic melanoma network is also given to show their potential application in

designing genetic therapeutics to reduce the risk of entering both aberrant phenotypes and other ambiguous states

Inferring the Number of Contributors to Mixed DNA Profiles

Intervention in Gene Regulatory Networks viaPhenotypically Constrained Control

PoliciesBased on Long-Run Behavior



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corresponding to complications or collateral damage. Experiments on both random network ensembles and the

melanoma network demonstrate that, in general, the new proposed control policies exhibit the desired performance. As

shown by intervening in the melanoma network, these control policies can potentially serve as future practical gene

therapeutic intervention strategies.

Genomic repositories increasingly include individual as well as reference sequences, which tend to share long identical

and near-identical strings of nucleotides. However, the sequential processing used by most compression algorithms,

and the volumes of data involved, mean that these long-range repetitions are not detected. An order-insensitive, disk-

based dictionary construction method can detect this repeated content and use it to compress collections of sequences.

We explore a dictionary construction method that improves repeat identification in large DNA data sets. Our adaptation,

Comrad, of an existing disk-based method identifies exact repeated content in collections of sequences with similarities

within and across the set of input sequences. Comrad compresses the data over multiple passes, which is an expensive

process, but allows Comrad to compress large data sets within reasonable time and space. Comrad allows for random

access to individual sequences and subsequences without decompressing the whole data set. Comrad has no

competitor in terms of the size of data sets that it can compress (extending to many hundreds of gigabytes) and, even

for smaller data sets, the results are competitive compared to alternatives; as an example, 39 S. cerevisiae genomes

compressed to 0.25 bits per base.

Although publicly accessible databases containing protein-protein interaction (PPI)-related information are important

resources to bench and in silico research scientists alike, the amount of time and effort required to keep them up to date

is often burdonsome. In an effort to help identify relevant PPI publications, text-mining tools, from the machine learning

discipline, can be applied to help in this process. Here, we describe and evaluate two document classification algorithms

that we submitted to the BioCreative II.5 PPI Classification Challenge Task. This task asked participants to design

classifiers for identifying documents containing PPI-related information in the primary literature, and evaluated them

against one another. One of our systems was the overall best-performing system submitted to the challenge task. It

utilizes a novel approach to k-nearest neighbor classification, which we describe here, and compare its performance to

those of two support vector machine-based classification systems, one of which was also evaluated in the challenge

task.

k-Information Gain Scaled Nearest Neighbors:A Novel Approach to Classifying Protein-

Protein Interaction-Related Documents

Manifold Adaptive Experimental Design for Text Categorization

Iterative Dictionary Construction for Compression of Large DNA Data Sets



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In many information processing tasks, labels are usually expensive and the unlabeled data points are abundant. To

reduce the cost on collecting labels, it is crucial to predict which unlabeled examples are the most informative, i.e.,

improve the classifier the most if they were labeled. Many active learning techniques have been proposed for text

categorization, such as SVMActive and Transductive Experimental Design. However, most of previous approaches try to

discover the discriminant structure of the data space, whereas the geometrical structure is not well respected. In this

paper, we propose a novel active learning algorithm which is performed in the data manifold adaptive kernel space. The

manifold structure is incorporated into the kernel space by using graph Laplacian. This way, the manifold adaptive

kernel space reflects the underlying geometry of the data. By minimizing the expected error with respect to the optimal

classifier, we can select the most representative and discriminative data points for labeling. Experimental results on text

categorization have demonstrated the effectiveness of our proposed approach.

We consider novel phylogenetic models with rate matrices that arise via the embedding of a progenitor model on a small

number of character states, into a target model on a larger number of character states. Adapting representation-

theoretic results from recent investigations of Markov invariants for the general rate matrix model, we give a prescription

for identifying and counting Markov invariants for such "symmetric embedded” models, and we provide enumerations of

these for the first few cases with a small number of character states. The simplest example is a target model on three

states, constructed from a general 2 state model; the "2 hookrightarrow 3” embedding. We show that for 2 taxa, there

exist two invariants of quadratic degree that can be used to directly infer pairwise distances from observed sequences

under this model. A simple simulation study verifies their theoretical expected values, and suggests that, given the

appropriateness of the model class, they have superior statistical properties than the standard (log) Det invariant (which

is of cubic degree for this case).

The reconstruction of evolutionary trees is one of the primary objectives in phylogenetics. Such a tree represents the

historical evolutionary relationship between different species or organisms. Tree comparisons are used for multiple

purposes, from unveiling the history of species to deciphering evolutionary associations among organisms and

geographical areas. In this paper, we propose a new method of defining distances between unrooted binary

phylogenetic trees that is especially useful for relatively large phylogenetic trees. Next, we investigate in detail the

properties of one example of these metrics, called the Matching Split distance, and describe how the general method

can be extended to nonbinary trees.

Matching Split Distance for Unrooted Binary Phylogenetic Trees

Memory Efficient Algorithms for Structural Alignment of RNAs with Pseudoknots

Markov Invariants for Phylogenetic Rate Matrices Derived from Embedded Submodels



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Posting In this paper, we consider the problem of structural alignment of a target RNA sequence of length n and a query

RNA sequence of length m with known secondary structure that may contain simple pseudoknots or embedded simple

pseudoknots. The best known algorithm for solving this problem runs in O(mn3) time for simple pseudoknot or O(mn4)

time for embedded simple pseudoknot with space complexity of O(mn3) for both structures, which require too much

memory making it infeasible for comparing noncoding RNAs (ncRNAs) with length several hundreds or more. We

propose memory efficient algorithms to solve the same problem. We reduce the space complexity to O(n3) for simple

pseudoknot and O(mn2 + n3) for embedded simple pseudoknot while maintaining the same time complexity. We also

show how to modify our algorithm to handle a restricted class of recursive simple pseudoknot which is found abundant

in real data with space complexity of O(mn2 + n3) and time complexity of O(mn4). Experimental results show that our

algorithms are feasible for comparing ncRNAs of length more than 500.

The rapid growth of scientific literature calls for automatic and efficient ways to facilitate extracting experimental data on

protein phosphorylation. Such information is of great value for biologists in studying cellular processes and diseases

such as cancer and diabetes. Existing approaches like RLIMS-P are mainly rule based. The performance lays much

reliance on the completeness of rules. We propose an SVM-based system known as MinePhos which outperforms

RLIMS-P in both precision and recall of information extraction when tested on a set of articles randomly chosen from

PubMed.

Molecular dynamics trajectories are very data intensive thereby limiting sharing and archival of such data. One possible

solution is compression of trajectory data. Here, trajectory compression based on conversion to the coarse-grained

model PRIMO is proposed. The compressed data are about one third of the original data and fast decompression is

possible with an analytical reconstruction procedure from PRIMO to all-atom representations. This protocol largely

preserves structural features and to a more limited extent also energetic features of the original trajectory.

Solid tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block tumor-induced

development of new blood vessels (angiogenesis) is an important approach in cancer treatment. The complexity of

angiogenesis presents both challenges and opportunities for cancer therapies. Intuitive approaches, such as blocking

Molecular Dynamics Trajectory Compressionwith a Coarse-Grained Model

Multiobjective Optimization Based-Approach forDiscovering Novel Cancer Therapies

MinePhos: A Literature Mining System for Protein Phoshphorylation Information

Extraction



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VegF activity, have yielded important therapies. But there maybe opportunities to alter nonintuitive targets either alone

or in combination. This paper describes the development of a high-fidelity simulation of angiogenesis and uses this as

the basis for a parallel search-based approach for the discovery of novel potential cancer treatments that inhibit blood

vessel growth. Discovering new therapies is viewed as a multiobjective combinatorial optimization over two competing

objectives: minimizing the estimated cost of practically developing the intervention while minimizing the simulated

oxygen provided to the tumor by angiogenesis. Results show the effectiveness of the search process by finding

interventions that are currently in use, and more interestingly, discovering potential new approaches that are

nonintuitive yet effective.

Network inference algorithms can assist life scientists in unraveling gene-regulatory systems on a molecular level. In

recent years, great attention has been drawn to the reconstruction of Boolean networks from time series. These need to

be binarized, as such networks model genes as binary variables (either "expressed” or "not expressed”). Common

binarization methods often cluster measurements or separate them according to statistical or information theoretic

characteristics and may require many data points to determine a robust threshold. Yet, time series measurements

frequently comprise only a small number of samples. To overcome this limitation, we propose a binarization that

incorporates measurements at multiple resolutions. We introduce two such binarization approaches which determine

thresholds based on limited numbers of samples and additionally provide a measure of threshold validity. Thus, network

reconstruction and further analysis can be restricted to genes with meaningful thresholds. This reduces the complexity

of network inference. The performance of our binarization algorithms was evaluated in network reconstruction

experiments using artificial data as well as real-world yeast expression time series. The new approaches yield

considerably improved correct network identification rates compared to other binarization techniques by effectively

reducing the amount of candidate networks.

Linkage analysis serves as a way of finding locations of genes that cause genetic diseases. Linkage studies have

facilitated the identification of several hundreds of human genes that can harbor mutations which by themselves lead to

a disease phenotype. The fundamental problem in linkage analysis is to identify regions whose allele is shared by all or

almost all affected members but by none or few unaffected members. Almost all the existing methods for linkage

analysis are for families with clearly given pedigrees. Little work has been done for the case where the sampled

individuals are closely related, but their pedigree is not known. This situation occurs very often when the individuals

share a common ancestor at least six generations ago. Solving this case will tremendously extend the use of linkage

analysis for finding genes that cause genetic diseases. In this paper, we propose a mathematical model (the shared

center problem) for inferring the allele-sharing status of a given set of individuals using a database of confirmed

Mutation Region Detection for Closely Related Individuals without a Known Pedigree

Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks



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haplotypes as reference. We show the NP-completeness of the shared center problem and present a ratio-2 polynomial-

time approximation algorithm for its minimization version (called the closest shared center problem). We then convert

the approximation algorithm into a heuristic algorithm for the shared center problem. Based on this heuristic, we finally

design a heuristic algorithm for mutation region detection. We further implement the algorithms to obtain a software

package. Our experimental data show that the software is both fast and accurate.

"Signal” alignments play critical roles in many clinical setting. This is the case of mass spectrometry (MS) data, an

important component of many types of proteomic analysis. A central problem occurs when one needs to integrate (MS)

data produced by different sources, e.g., different equipment and/or laboratories. In these cases, some form of "data

integration” or "data fusion” may be necessary in order to discard some source-specific aspects and improve the ability

to perform a classification task such as inferring the "disease classes” of patients. The need for new high-performance

data alignments methods is therefore particularly important in these contexts. In this paper, we propose an approach

based both on an information theory perspective, generally used in a feature construction problem, and the application

of a mathematical programming task (i.e., the weighted bipartite matching problem). We present the results of a

competitive analysis of our method against other approaches. The analysis was conducted on data from

plasma/ethylenediaminetetraacetic acid of "control” and Alzheimer patients collected from three different hospitals. The

results point to a significant performance advantage of our method with respect to the competing ones tested

We study the well-known Largest Common Point-set (LCP) under Bottleneck Distance Problem. Given two proteins o

and 6 (as sequences of points in three-dimensional space) and a distance cutoff ζ, the goal is to find a spatial

superposition and an alignment that maximizes the number of pairs of points from a and b that can be fit under the

distance ζ from each other. The best to date algorithms for approximate and exact solution to this problem run in time

O(n8) and O(n32), respectively, where n represents protein length. This work improves runtime of the approximation

algorithm and the expected runtime of the algorithm for absolute optimum for both order-dependent and order-

independent alignments. More specifically, our algorithms for near-optimal and optimal sequential alignments run in

time O(n7log n) and O(n14 log n), respectively. For nonsequential alignments, corresponding running times are O(n7.5)

and O(n14.5).

Mutual Information Optimization for Mass Spectra Data Alignment

On Complexity of Protein Structure Alignment Problem under Distance Constraint

On Parameter Synthesis by Parallel Model Checking



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An important problem in current computational systems biology is to analyze models of biological systems dynamics

under parameter uncertainty. This paper presents a novel algorithm for parameter synthesis based on parallel model

checking. The algorithm is conceptually universal with respect to the modeling approach employed. We introduce the

algorithm, show its scalability, and examine its applicability on several biological models.

An important problem in current computational systems biology is to analyze models of biological systems dynamics

under parameter uncertainty. This paper presents a novel algorithm for parameter synthesis based on parallel model

checking. The algorithm is conceptually universal with respect to the modeling approach employed. We introduce the

algorithm, show its scalability, and examine its applicability on several biological models.

Rooted phylogenetic networks are often used to represent conflicting phylogenetic signals. Given a set of clusters, a

network is said to represent these clusters in the softwiredsense if, for each cluster in the input set, at least one tree

embedded in the network contains that cluster. Motivated by parsimony we might wish to construct such a network

using as few reticulations as possible, or minimizing the level of the network, i.e., the maximum number of reticulations

used in any "tangled" region of the network. Although these are NP-hard problems, here we prove that, for every fixed k

≥ 0, it is polynomial-time solvable to construct a phylogenetic network with level equal to k representing a cluster set, or

to determine that no such network exists. However, this algorithm does not lend itself to a practical implementation. We

also prove that the comparatively efficient CASS algorithm correctly solves this problem (and also minimizes the

reticulation number) when input clusters are obtained from two not necessarily binary gene trees on the same set of

taxa but does not always minimize level for general cluster sets. Finally, we describe a new algorithm which generates in

polynomial-time all binary phylogenetic networks with exactly r reticulations representing a set of input clusters (for

every fixed r ≥ 0)

We address the problem of realizing a given distance matrix by a planar phylogenetic network with a minimum number

of faces. With the help of the popular software SplitsTree4, we start by approximating the distance matrix with a distance

metric that is a linear combination of circular splits. The main results of this paper are the necessary and sufficient

conditions for the existence of a network with a single face. We show how such a network can be constructed, and we

present a heuristic for constructing a network with few faces using the first algorithm as the base case. Experimental

On the Application of Active Learning and Gaussian Processes in Postcryo preservation

Cell Membrane Integrity Experiments

On the Elusiveness of Clusters

Optimizing Phylogenetic Networks for Circular Split Systems



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results on biological data show that this heuristic algorithm can produce phylogenetic networks with far fewer faces

than the ones computed by SplitsTree4, without affecting the approximation of the distance matrix.

This The focus of this paper is the problem of finding all nested common intervals of two general sequences. Depending

on the treatment one wants to apply to duplicate genes, Blin et al. introduced three models to define nested common

intervals of two sequences: the uniqueness, the free-inclusion, and the bijection models. We consider all the three

models. For the uniqueness and the bijection models, we give O(n + Nout)-time algorithms, where Nout denotes the size

of the output. For the free-inclusion model, we give an O(n1+ε + Nout)-time algorithm, where ε >; 0 is an arbitrarily small

constant. We also present an upper bound on the size of the output for each model. For the uniqueness and the free-

inclusion models, we show that Nout = O(n2). Let C = ΣgϵΓ o1(g)o2(5), where Γ is the set of distinct genes, and o1(g) and

o2(g) are, respectively, the numbers of copies of g in the two given sequences. For the bijection model, we show that

Nout = O(Cn). In this paper, we also study the problem of finding all approximate nested common intervals of two

sequences on the bijection model. An O(δn + Nout)-time algorithm is presented, where δ denotes the maximum number

of allowed gaps. In addition, we show that for this problem Nout is O(δn3).

This paper gives a comprehensive review of the application of metaheuristics to optimization problems in systems

biology, mainly focusing on the parameter estimation problem (also called the inverse problem or model calibration). It

is intended for either the system biologist who wishes to learn more about the various optimization techniques available

and/or the metaheuristic optimizer who is interested in applying such techniques to problems in systems biology. First,

the parameter estimation problems emerging from different areas of systems biology are described from the point of

view of machine learning. Brief descriptions of various metaheuristics developed for these problems follow, along with

outlines of their advantages and disadvantages. Several important issues in applying metaheuristics to the systems

biology modeling problem are addressed, including the reliability and identifiability of model parameters, optimal design

of experiments, and so on. Finally, we highlight some possible future research directions in this field.

Searching tandem mass spectra against a protein database has been a mainstream method for peptide identification.

Improving peptide identification results by ranking true Peptide-Spectrum Matches (PSMs) over their false counterparts

leads to the development of various reranking algorithms. In peptide reranking, discriminative information is essential to

Output-Sensitive Algorithms for Finding the Nested Common Intervals of Two General

Sequences

Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive

Review

Peptide Reranking with Protein-Peptide Correspondence and Precursor Peak Intensity

Information



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distinguish true PSMs from false PSMs. Generally, most peptide reranking methods obtain discriminative information

directly from database search scores or by training machine learning models. Information in the protein database and

MS1 spectra (i.e., single stage MS spectra) is ignored. In this paper, we propose to use information in the protein

database and MS1 spectra to rerank peptide identification results. To quantitatively analyze their effects to peptide

reranking results, three peptide reranking methods are proposed: PPMRanker, PPIRanker, and MIRanker. PPMRanker

only uses Protein-Peptide Map (PPM) information from the protein database, PPIRanker only uses Precursor Peak

Intensity (PPI) information, and MIRanker employs both PPM information and PPI information. According to our

experiments on a standard protein mixture data set, a human data set and a mouse data set, PPMRanker and MIRanker

achieve better peptide reranking results than PetideProphet, PeptideProphet+NSP (number of sibling peptides) and a

score regularization method SRPI.

Search We present a new computational method for predicting ligand binding residues and functional sites in protein

sequences. These residues and sites tend to be not only conserved, but also exhibit strong correlation due to the

selection pressure during evolution in order to maintain the required structure and/or function. To explore the effect of

correlations among multiple positions in the sequences, the method uses graph theoretic clustering and kernel-based

canonical correlation analysis (kCCA) to identify binding and functional sites in protein sequences as the residues that

exhibit strong correlation between the residues' evolutionary characterization at the sites and the structure-based

functional classification of the proteins in the context of a functional family. The results of testing the method on two

well-curated data sets show that the prediction accuracy as measured by Receiver Operating Characteristic (ROC)

scores improves significantly when multipositional correlations are accounted for.

Prediction of binding sites from sequence can significantly help toward determining the function of uncharacterized

proteins on a genomic scale. The task is highly challenging due to the enormous amount of alternative candidate

configurations. Previous research has only considered this prediction problem starting from 3D information. When

starting from sequence alone, only methods that predict the bonding state of selected residues are available. The sole

exception consists of pattern-based approaches, which rely on very specific motifs and cannot be applied to discover

truly novel sites. We develop new algorithmic ideas based on structured-output learning for determining transition-

metal-binding sites coordinated by cysteines and histidines. The inference step (retrieving the best scoring output) is

intractable for general output types (i.e., general graphs). However, under the assumption that no residue can coordinate

more than one metal ion, we prove that metal binding has the algebraic structure of a matroid, allowing us to employ a

very efficient greedy algorithm. We test our predictor in a highly stringent setting where the training set consists of

Predicting Ligand Binding Residues and Functional Sites Using Multipositional

Correlations with Graph Theoretic Clustering and Kernel CCA

Predicting Metal-Binding Sites from Protein Sequence



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protein chains belonging to SCOP folds different from the ones used for accuracy estimation. In this setting, our

predictor achieves 56 percent precision and 60 percent recall in the identification of ligand-ion bonds.

Assigning biological functions to uncharacterized proteins is a fundamental problem in the postgenomic era. The

increasing availability of large amounts of data on protein-protein interactions (PPIs) has led to the emergence of a

considerable number of computational methods for determining protein function in the context of a network. These

algorithms, however, treat each functional class in isolation and thereby often suffer from the difficulty of the scarcity of

labeled data. In reality, different functional classes are naturally dependent on one another. We propose a new algorithm,

Multi-label Correlated Semi-supervised Learning (MCSL), to incorporate the intrinsic correlations among functional

classes into protein function prediction by leveraging the relationships provided by the PPI network and the functional

class network. The guiding intuition is that the classification function should be sufficiently smooth on subgraphs where

the respective topologies of these two networks are a good match. We encode this intuition as regularized learning with

intraclass and interclass consistency, which can be understood as an extension of the graph-based learning with local

and global consistency (LGC) method. Cross validation on the yeast proteome illustrates that MCSL consistently

outperforms several state-of-the-art methods. Most notably, it effectively overcomes the problem associated with

scarcity of label data. The supplementary files are freely available at http://sites.google.com/site/csaijiang/MCSL

Detecting protein complexes from protein interaction networks is one major task in the postgenome era. Previous

developed computational algorithms identifying complexes mainly focus on graph partition or dense region finding.

Most of these traditional algorithms cannot discover overlapping complexes which really exist in the protein-protein

interaction (PPI) networks. Even if some density-based methods have been developed to identify overlapping

complexes, they are not able to discover complexes that include peripheral proteins. In this study, motivated by recent

successful application of generative network model to describe the generation process of PPI networks and to detect

communities from social networks, we develop a regularized sparse generative network model (RSGNM), by adding

another process that generates propensities using exponential distribution and incorporating Laplacian regularizer into

an existing generative network model, for protein complexes identification. By assuming that the propensities are

generated using exponential distribution, the estimators of propensities will be sparse, which not only has good

biological interpretation but also helps to control the overlapping rate among detected complexes. And the Laplacian

regularizer will lead to the estimators of propensities more smooth on interaction networks. Experimental results on

three yeast PPI networks show that RSGNM outperforms six previous competing algorithms in terms of the quality of

detected complexes. In addition, RSGNM is able to detect overlapping complexes and complexes including peripheral

proteins simultaneously. These results give new insights about the importance of generative network models in protein

complexes identification.

Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning

Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized

Sparse Generative Network Model



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Bistability/Multistability has been found in many biological systems including genetic memory circuits. Proper

characterization of system stability helps to understand biological functions and has potential applications in fields

such as synthetic biology. Existing methods of analyzing bistability are either qualitative or in a static way. Assuming

the circuit is in a steady state, the latter can only reveal the susceptibility of the stability to injected DC noises. However,

this can be inappropriate and inadequate as dynamics are crucial for many biological networks. In this paper, we

quantitatively characterize the dynamic stability of a genetic conditional memory circuit by developing new dynamic

noise margin (DNM) concepts and associated algorithms based on system theory. Taking into account the duration of

the noisy perturbation, the DNMs are more general cases of their static counterparts. Using our techniques, we analyze

the noise immunity of the memory circuit and derive insights on dynamic hold and write operations. Considering cell-to-

cell variations, our parametric analysis reveals that the dynamic stability of the memory circuit has significantly varying

sensitivities to underlying biochemical reactions attributable to differences in structure, time scales, and nonlinear

interactions between reactions. With proper extensions, our techniques are broadly applicable to other multistable

biological systems.

In vitro assembly of intermediate filaments from tetrameric vimentin consists of a very rapid phase of tetramers laterally

associating into unit-length filaments and a slow phase of filament elongation. We focus in this paper on a systematic

quantitative investigation of two molecular models for filament assembly, recently proposed in (Kirmse et al. J. Biol.

Chem. 282, 52 (2007), 18563-18572), through mathematical modeling, model fitting, and model validation. We analyze the

quantitative contribution of each filament elongation strategy: with tetramers, with unit-length filaments, with longer

filaments, or combinations thereof. In each case, we discuss the numerical fitting of the model with respect to one set of

data, and its separate validation with respect to a second, different set of data. We introduce a high-resolution model for

vimentin filament self-assembly, able to capture the detailed dynamics of filaments of arbitrary length. This provides

much more predictive power for the model, in comparison to previous models where only the mean length of all

filaments in the solution could be analyzed. We show how kinetic observations on low-resolution models can be

extrapolated to the high-resolution model and used for lowering its complexity.

Bacteria evolved cell to cell communication processes to gain information about their environment and regulate gene

expression. Quorum sensing is such a process in which signaling molecules, called autoinducers, are produced,

Quantifying Dynamic Stability of Genetic Memory Circuits

Quantitative Analysis of the Self-Assembly Strategies of Intermediate Filaments from

Tetrameric Vimentin

Quantum Gate Circuit Model of Signal Integration in Bacterial Quorum Sensing



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secreted and detected. In several cases bacteria use more than one autoinducers and integrate the information

conveyed by them. It has not yet been explained adequately why bacteria evolved such signal integration circuits and

what can learn about their environments using more than one autoinducers since all signaling pathways merge in one.

Here quantum information theory, which includes classical information theory as a special case, is used to construct a

quantum gate circuit that reproduces recent experimental results. Although the conditions in which biosystems exist do

not allow for the appearance of quantum mechanical phenomena, the powerful computation tools of quantum

information processing can be carefully used to cope with signal and information processing by these complex

systems. A simulation algorithm based on this model has been developed and numerical experiments that analyze the

dynamical operation of the quorum sensing circuit were performed for various cases of autoinducer variations, which

revealed that these variations contain significant information about the environment in which bacteria exist.

The experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-

consuming, while computational methods to infer these networks provide only modest accuracy. The latter can be

attributed partly to the limitations of a single-organism approach. Computational biology has long used comparative and

evolutionary approaches to extend the reach and accuracy of its analyses. In this paper, we describe ProPhyC, a

probabilistic phylogenetic model and associated inference algorithms, designed to improve the inference of regulatory

networks for a family of organisms by using known evolutionary relationships among these organisms. ProPhyC can be

used with various network evolutionary models and any existing inference method. Extensive experimental results on

both biological and synthetic data confirm that our model (through its associated refinement algorithms) yields

substantial improvement in the quality of inferred networks over all current methods. We also compare ProPhyC with a

transfer learning approach we design. This approach also uses phylogenetic relationships while inferring regulatory

networks for a family of organisms. Using similar input information but designed in a very different framework, this

transfer learning approach does not perform better than ProPhyC, which indicates that ProPhyC makes good use of the

evolutionary information.

Accurate identification of protein secondary structures is beneficial to understand three-dimensional structures of

biological macromolecules. In this paper, a novel refined classification framework is proposed, which treats alpha-helix

identification as a machine learning problem by representing each voxel in the density map with its Spherical Harmonic

Descriptors (SHD). An energy function is defined to provide statistical analysis of its identification performance, which

can be applied to all the α-helix identification approaches. Comparing with other existing α-helix identification methods

for intermediate resolution electron density maps, the experimental results demonstrate that our approach gives the

best identification accuracy and is more robust to the noise.

RENNSH: A Novel α-Helix Identification Approach for Intermediate Resolution Electron

Density Maps

Refining Regulatory Networks through Phylogenetic Transfer of Information



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We present in this study a new approach to code protein side-chain conformations into hexagon substructures.

Classical side-chain packing methods consist of two steps: first, side-chain conformations, known as rotamers, are

extracted from known protein structures as candidates for each residue; second, a searching method along with an

energy function is used to resolve conflicts among residues and to optimize the combinations of side chain

conformations for all residues. These methods benefit from the fact that the number of possible side-chain

conformations is limited, and the rotamer candidates are readily extracted; however, these methods also suffer from the

inaccuracy of energy functions. Inspired by threading and Ab Initio approaches to protein structure prediction, we

propose to use hexagon substructures to implicitly capture subtle issues of energy functions. Our initial results indicate

that even without guidance from an energy function, hexagon structures alone can capture side-chain conformations at

an accuracy of 83.8 percent, higher than 82.6 percent by the state-of-art side-chain packing methods.

Regulation of gene expression is a carefully regulated phenomenon in the cell. "Reverse-engineering” algorithms try to

reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles

(microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are

necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to

be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a

parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene

expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We

successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene

expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to

discover "communities” within the gene network. Network communities are enriched for genes involved in the same

biological functions. The inferred network was used to identify two mitochondrial proteins.

Tumor classification based on Gene Expression Profiles (GEPs), which is of great benefit to the accurate diagnosis and

personalized treatment for different types of tumor, has drawn a great attention in recent years. This paper proposes a

novel tumor classification method based on correlation filters to identify the overall pattern of tumor subtype hidden in

differentially expressed genes. Concretely, two correlation filters, i.e., Minimum Average Correlation Energy (MACE) and

Optimal Tradeoff Synthetic Discriminant Function (OTSDF), are introduced to determine whether a test sample matches

Residues with Similar Hexagon Neighborhoods Share Similar Side-Chain Conformations

Reverse Engineering and Analysis of Genome-Wide Gene Regulatory Networks from

Gene Expression Profiles Using High-Performance Computing

Software Fault Prediction Using Quad Tree-Based K-Means Clustering Algorithm



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the templates synthesized for each subclass. The experiments on six publicly available data sets indicate that the

proposed method is robust to noise, and can more effectively avoid the effects of dimensionality curse. Compared with

many model-based methods, the correlation filter-based method can achieve better performance when balanced training

sets are exploited to synthesize the templates. Particularly, the proposed method can detect the similarity of overall

pattern while ignoring small mismatches between test sample and the synthesized template. And it performs well even if

only a few training samples are available. More importantly, the experimental results can be visually represented, which

is helpful for the further analysis of results.

Motivated by the trend of genome sequencing without completing the sequence of the whole genomes, a problem on

filling an incomplete multichromosomal genome (or scaffold) I with respect to a complete target genome G was studied.

The objective is to minimize the resulting genomic distance between I^{prime } and G, where I^{prime } is the

corresponding filled scaffold. We call this problem the one-sided scaffold filling problem. In this paper, we conduct a

systematic study for the scaffold filling problem under the breakpoint distance and its variants, for both

unichromosomal and multichromosomal genomes (with and without gene repetitions). When the input genome contains

no gene repetition (i.e., is a fragment of a permutation), we show that the two-sided scaffold filling problem (i.e., G is also

incomplete) is polynomially solvable for unichromosomal genomes under the breakpoint distance and for

multichromosomal genomes under the genomic (or DCJ—Double-Cut-and-Join) distance. However, when the input

genome contains some repeated genes, even the one-sided scaffold filling problem becomes NP-complete when the

similarity measure is the maximum number of adjacencies between two sequences. For this problem, we also present

efficient constant-factor approximation algorithms: factor-2 for the general case and factor 1.33 for the one-sided case.

Studying biological networks at topological level is a major issue in computational biology studies and simulation is

often used in this context, either to assess reverse engineering algorithms or to investigate how topological properties

depend on network parameters. In both contexts, it is desirable for a topology simulator to reproduce the current

knowledge on biological networks, to be able to generate a number of networks with the same properties and to be

flexible with respect to the possibility to mimic networks of different organisms. We propose a biological network

topology simulator, SimBioNeT, in which module structures of different type and size are replicated at different level of

network organization and interconnected, so to obtain the desired degree distribution, e.g., scale free, and a clustering

coefficient constant with the number of nodes in the network, a typical characteristic of biological networks. Empirical

assessment of the ability of the simulator to reproduce characteristic properties of biological network and comparison

with E. coli and S. cerevisiae transcriptional networks demonstrates the effectiveness of our proposal.

Scaffold Filling under the Break point and Related Distances

SimBioNeT: A Simulator of Biological Network Topology



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Space is a very important aspect in the simulation of biochemical systems; recently, the need for simulation algorithms

able to cope with space is becoming more and more compelling. Complex and detailed models of biochemical systems

need to deal with the movement of single molecules and particles, taking into consideration localized fluctuations,

transportation phenomena, and diffusion. A common drawback of spatial models lies in their complexity: models can

become very large, and their simulation could be time consuming, especially if we want to capture the systems behavior

in a reliable way using stochastic methods in conjunction with a high spatial resolution. In order to deliver the promise

done by systems biology to be able to understand a system as whole, we need to scale up the size of models we are

able to simulate, moving from sequential to parallel simulation algorithms. In this paper, we analyze Smoldyn, a widely

diffused algorithm for stochastic simulation of chemical reactions with spatial resolution and single molecule detail, and

we propose an alternative, innovative implementation that exploits the parallelism of Graphics Processing Units (GPUs).

The implementation executes the most computational demanding steps (computation of diffusion, unimolecular, and

bimolecular reaction, as well as the most common cases of molecule-surface interaction) on the GPU, computing them

in parallel on each molecule of the system. The implementation offers good speed-ups and real time, high quality

graphics output.

Availability of an effective tool for protein multiple structural alignment (MSTA) is essential for discovery and analysis of

biologically significant structural motifs that can help solve functional annotation and drug design problems. Existing

MSTA methods collect residue correspondences mostly through pairwise comparison of consecutive fragments, which

can lead to suboptimal alignments, especially when the similarity among the proteins is low. We introduce a novel

strategy based on: building a contact-window based motif library from the protein structural data, discovery and

extension of common alignment seeds from this library, and optimal superimposition of multiple structures according to

these alignment seeds by an enhanced partial order curve comparison method. The ability of our strategy to detect

multiple correspondences simultaneously, to catch alignments globally, and to support flexible alignments, endorse a

sensitive and robust automated algorithm that can expose similarities among protein structures even under low

similarity conditions. Our method yields better alignment results compared to other popular MSTA methods, on several

protein structure data sets that span various structural folds and represent different protein similarity levels.

Smoldyn on Graphics Processing Units: Massively Parallel Brownian Dynamics

Simulations

Smolign: A Spatial Motifs-Based Protein Multiple Structural Alignment Method

Stable Gene Selection from Microarray Data via Sample Weighting



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Feature selection from gene expression microarray data is a widely used technique for selecting candidate genes in

various cancer studies. Besides predictive ability of the selected genes, an important aspect in evaluating a selection

method is the stability of the selected genes. Experts instinctively have high confidence in the result of a selection

method that selects similar sets of genes under some variations to the samples. However, a common problem of

existing feature selection methods for gene expression data is that the selected genes by the same method often vary

significantly with sample variations. In this work, we propose a general framework of sample weighting to improve the

stability of feature selection methods under sample variations. The framework first weights each sample in a given

training set according to its influence to the estimation of feature relevance, and then provides the weighted training set

to a feature selection method. We also develop an efficient margin-based sample weighting algorithm under this

framework. Experiments on a set of microarray data sets show that the proposed algorithm significantly improves the

stability of representative feature selection algorithms such as SVM-RFE and ReliefF, without sacrificing their

classification performance. Moreover, the proposed algorithm also leads to more stable gene signatures than the state-

of-the-art ensemble method, particularly for small signature sizes.

Gene expression models play a key role to understand the mechanisms of gene regulation whose aspects are grade and

switch-like responses. Though many stochastic approaches attempt to explain the gene expression mechanisms, the

Gillespie algorithm which is commonly used to simulate the stochastic models hardly explain the switch-like behaviors

of gene responses. In this study, we propose a stochastic gene expression model which can describe the switch-like

behaviors of gene responses by employing Hill functions to the conventional Gillespie algorithm. We assume eight

processes of gene expression and their biologically appropriate reaction rates are estimated based on published

literatures. Our negative regulatory model shows that the modified Gillespie algorithm successfully describes the

switch-like behaviors of gene responses, which is consistent with a published experimental study. We observe that the

state of the system of the toggled switch model is rarely changed since the Hill function prevents the activation of

involved proteins when their concentrations stay at low level. In ScbA/ScbR system which can control the antibiotic

metabolite production of microorganisms, our proposed stochastic approach successfully models its switch-like gene

response and oscillatory expressions.

One of the major research directions in bioinformatics is that of assigning superfamily classification to a given set of

proteins. The classification reflects the structural, evolutionary, and functional relatedness. These relationships are

embodied in a hierarchical classification, such as the Structural Classification of Protein (SCOP), which is mostly

manually curated. Such a classification is essential for the structural and functional analyses of proteins. Yet a large

number of proteins remain unclassified. In this study, we have proposed an unsupervised machine learning approach to

Stochastic Gene Expression Modeling with Hill Function for Switch-Like Gene Responses

Structural SCOP Super family Level Classification Using Unsupervised Machine Learning



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classify and assign a given set of proteins to SCOP superfamilies. In the method, we have constructed a database and

similarity matrix using P-values obtained from an all-against-all BLAST run and trained the network with the ART2

unsupervised learning algorithm using the rows of the similarity matrix as input vectors, enabling the trained network to

classify the proteins from 0.82 to 0.97 f-measure accuracy. The performance of ART2 has been compared with that of

spectral clustering, Random forest, SVM, and HHpred. ART2 performs better than the others except HHpred. HHpred

performs better than ART2 and the sum of errors is smaller than that of the other methods evaluated

Computational methods for predicting protein subcellular localization have used various types of features, including N-

terminal sorting signals, amino acid compositions, and text annotations from protein databases. Our approach does not

use biological knowledge such as the sorting signals or homologues, but use just protein sequence information. The

method divides a protein sequence into short k-mer sequence fragments which can be mapped to word features in

document classification. A large number of class association rules are mined from the protein sequence examples that

range from the N-terminus to the C-terminus. Then, a boosting algorithm is applied to those rules to build up a final

classifier. Experimental results using benchmark data sets show that our method is excellent in terms of both the

classification performance and the test coverage. The result also implies that the k-mer sequence features which

determine subcellular locations do not necessarily exist in specific positions of a protein sequence.

We show that two important problems that have applications in computational biology are ASP-complete, which implies

that, given a solution to a problem, it is NP-complete to decide if another solution exists. We show first that a variation of

BETWEENNESS, which is the underlying problem of questions related to radiation hybrid mapping, is ASP-complete.

Subsequently, we use that result to show that QUARTET COMPATIBILITY, a fundamental problem in phylogenetics that

asks whether a set of quartets can be represented by a parent tree, is also ASP-complete. The latter result shows that

Steel's QUARTET CHALLENGE, which asks whether a solution to QUARTET COMPATIBILITY is unique, is coNP-

complete.

Enormous data collection efforts and improvements in technology have made large genome-wide association studies a

promising approach for better understanding the genetics of common diseases. Still, the knowledge gained from these

studies may be extended even further by testing the hypothesis that genetic susceptibility is due to the combined effect

of multiple variants or interactions between variants. Here, we explore and evaluate the use of a genetic algorithm to

Subcellular Localization Prediction through Boosting Association Rules

The Complexity of Finding Multiple Solutions to Betweenness and Quartet Compatibility

The GA and the GWAS: Using Genetic Algorithms to Search for Multilocus Associations



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discover groups of SNPs (of size 2, 3, or 4) that are jointly associated with bipolar disorder. The algorithm is guided by

the structure of a gene interaction network, and is able to find groups of SNPs that are strongly associated with the

disease, while performing far fewer statistical tests than other methods.

Metagenomics enables the study of unculturable microorganisms in different environments directly. Discriminating

between the compositional differences of metagenomes is an important and challenging problem. Several distance

functions have been proposed to estimate the differences based on functional profiles or taxonomic distributions;

however, the strengths and limitations of such functions are still unclear. Initially, we analyzed three well-known

distance functions and found very little difference between them in the clustering of samples. This motivated us to

incorporate suitable normalizations and phylogenetic information into the functions so that we could cluster samples

from both real and synthetic data sets. The results indicate significant improvement in sample clustering over that

derived by rank-based normalization with phylogenetic information, regardless of whether the samples are from real or

synthetic microbiomes. Furthermore, our findings suggest that considering suitable normalizations and phylogenetic

information is essential when designing distance functions for estimating the differences between metagenomes. We

conclude that incorporating rank-based normalization with phylogenetic information into the distance functions helps

achieve reliable clustering results.

A Maximum Agreement SubTree (MAST) is a largest subtree common to a set of trees and serves as a summary of

common substructure in the trees. A single MAST can be misleading, however, since there can be an exponential

number of MASTs, and two MASTs for the same tree set do not even necessarily share any leaves. In this paper, we

introduce the notion of the Kernel Agreement SubTree (KAST), which is the summary of the common substructure in all

MASTs, and show that it can be calculated in polynomial time (for trees with bounded degree). Suppose the input trees

represent competing hypotheses for a particular phylogeny. We explore the utility of the KAST as a method to discern

the common structure of confidence, and as a measure of how confident we are in a given tree set. We also show the

trend of the KAST, as compared to other consensus methods, on the set of all trees visited during a Bayesian analysis

of flatworm genomes.

Important achievements in traditional biology have deepened the knowledge about living systems leading to an

extensive identification of parts-list of the cell as well as of the interactions among biochemical species responsible for

The Impact of Normalization and Phylogenetic Information on Estimating the Distancefor

Metagenomes

The Kernel of Maximum Agreement Subtrees

The Relevance of Topology in Parallel Simulation of Biological Networks



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cell's regulation. Such an expanding knowledge also introduces new issues. For example, the increasing

comprehension of the interdependencies between pathways (pathways cross-talk) has resulted, on one hand, in the

growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge

remains unchanged: to be able to assemble the knowledge of every "piece” of a system in order to figure out the

behavior of the whole (integrative approach). In light of these considerations, high performance computing plays a

fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although

widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high

dimensional systems. Here, we introduce and discuss a methodology aimed at alleviating the burden of simulating

complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting

the computational space of a simulation trace and delegating the computation of fragments to a number of parallel

processes.

Maximum Using the Matt structure alignment program, we take a tour of protein space, producing a hierarchical

clustering scheme that divides protein structural domains into clusters based on geometric dissimilarity. While it was

known that purely structural, geometric, distance-based measures of structural similarity, such as Dali/FSSP, could

largely replicate hand-curated schemes such as SCOP at the family level, it was an open question as to whether any

such scheme could approximate SCOP at the more distant superfamily and fold levels. We partially answer this question

in the affirmative, by designing a clustering scheme based on Matt that approximately matches SCOP at the superfamily

level, and demonstrates qualitative differences in performance between Matt and DaliLite. Implications for the debate

over the organization of protein fold space are discussed. Based on our clustering of protein space, we introduce the

Mattbench benchmark set, a new collection of structural alignments useful for testing sequence aligners on more

distantly homologous proteins.

Binary (0,1) matrices, commonly known as transactional databases, can represent many application data, including

gene-phenotype data where "1” represents a confirmed gene-phenotype relation and "0” represents an unknown

relation. It is natural to ask what information is hidden behind these "0”s and "1”s. Unfortunately, recent matrix

completion methods, though very effective in many cases, are less likely to infer something interesting from these (0,1)-

matrices. To answer this challenge, we propose Ind Evi, a very succinct and effective algorithm to perform independent-

evidence-based transactional database transformation. Each entry of a (0,1)-matrix is evaluated by "independent

evidence” (maximal supporting patterns) extracted from the whole matrix for this entry. The value of an entry, regardless

Touring Protein Space with Matt

Transactional Database Transformation and Its Application in Prioritizing Human Disease

Genes



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of its value as 0 or 1, has completely no effect for its independent evidence. The experiment on a gene-phenotype

database shows that our method is highly promising in ranking candidate genes and predicting unknown disease genes.

In systems biology, a number of detailed genetic regulatory networks models have been proposed that are capable of

modeling the fine-scale dynamics of gene expression. However, limitations on the type and sampling frequency of

experimental data often prevent the parameter estimation of the detailed models. Furthermore, the high computational

complexity involved in the simulation of a detailed model restricts its use. In such a scenario, reduced-order models

capturing the coarse-scale behavior of the network are frequently applied. In this paper, we analyze the dynamics of a

reduced-order Markov Chain model approximating a detailed Stochastic Master Equation model. Utilizing a reduction

mapping that maintains the aggregated steady-state probability distribution of stochastic master equation models, we

provide bounds on the deviation of the Markov Chain transient distribution from the transient aggregated distributions

of the stochastic master equation model.

General Purpose Graphic Processing Units (GPGPUs) constitute an inexpensive resource for computing-intensive

applications that could exploit an intrinsic fine-grain parallelism. This paper presents the design and implementation in

GPGPUs of an exact alignment tool for nucleotide sequences based on the Burrows-Wheeler Transform. We compare

this algorithm with state-of-the-art implementations of the same algorithm over standard CPUs, and considering the

same conditions in terms of I/O. Excluding disk transfers, the implementation of the algorithm in GPUs shows a speedup

larger than 12{times}, when compared to CPU execution. This implementation exploits the parallelism by concurrently

searching different sequences on the same reference search tree, maximizing memory locality and ensuring a

symmetric access to the data. The paper describes the behavior of the algorithm in GPU, showing a good scalability in

the performance, only limited by the size of the GPU inner memory.

Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks

Using GPUs for the Exact Alignment of Short-Read Genetic Sequences by Means of the

Burrows-Wheeler Transform