cd40 ligand and tumor necro sis factor alpha , the cells acquire a mature
DESCRIPTION
CD40 ligand and tumor necro sis factor alpha , the cells acquire a mature phenotype of dendritic cells that is characterized by up - regulation of human leukocy te antigen ( CD80 , CD86 , CD40 and CD54 and appearance of CD83 . These. Erik van Mulligen Martijn Schuemie - PowerPoint PPT PresentationTRANSCRIPT
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CD40 ligand and tumor necrosis factor alpha, the cells acquire a mature phenotype of dendritic cells that is characterized by up-regulation of human leukocy
te antigen (CD80, CD86, CD40and CD54 and appearance of CD83. These
Second Order Semantic Enrichmentand the role of Wiki’s for Professionals
Second Order Semantic Enrichmentand the role of Wiki’s for Professionals
•Peter Bram ‘t Hoen•Ellen Sterrenburg•Herman van Haagen•Allessandro Botelho-Bovo•Judith Boer•Johan den Dunnen •Gert Jan van Ommen
•Peter Bram ‘t Hoen•Ellen Sterrenburg•Herman van Haagen•Allessandro Botelho-Bovo•Judith Boer•Johan den Dunnen •Gert Jan van Ommen
•Erik van Mulligen•Martijn Schuemie•Rob Jelier•Antoine Velthoven•Christina Hettne•Jan Kors•Johan van der Lei
•Erik van Mulligen•Martijn Schuemie•Rob Jelier•Antoine Velthoven•Christina Hettne•Jan Kors•Johan van der Lei
•Christine Chichester•Erik van Mulligen•Marc Weeber•Kevin Kalupsen•Reuben Christie•Jacintha van Beemen•Nickolas Barris•Albert Mons
•Christine Chichester•Erik van Mulligen•Marc Weeber•Kevin Kalupsen•Reuben Christie•Jacintha van Beemen•Nickolas Barris•Albert Mons
•Gerard Meijssen•Erik Moeller•Peter Jan Roes•Karsten Uil•Siebrand Mazeland•Sabine Cretella
•Gerard Meijssen•Erik Moeller•Peter Jan Roes•Karsten Uil•Siebrand Mazeland•Sabine Cretella
Barend MonsBarend Mons
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The ConsortiumOpen Access Semantic Support Technology
For on-line Knowledge Tracking, Discovery and Management
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WikiProfessional
Semantic Web workspaces for scientistsenabling real time knowledge exchange and
exploration
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Many challenges in current bomedical research
• Volume of data (both high troughput and text)• Complexity • Distributed systems and databases• Incompatible data formats• Multi-disciplinarity• Multi-linguality• Ambiguity of terminology• Inability to share Knowledge• Globalization of knowledge
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•Repetition of facts is of great value for the readability of individual papers, •but the fact itself is a single unit of information, and needs no repetition.
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– A defining characteristic of wiki technology is the ease with which pages can be created and updated. Generally, there is no review before modifications are accepted.
The Million Minds Approach
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Personal Communication Johan den Dunnen.Personal Communication Johan den Dunnen.
Websites such as www.dmd.nl are increasingly cited in the literatureWebsites such as www.dmd.nl are increasingly cited in the literature
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0
1000
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1 researchgroup
2 or moregroups
genes/proteins
The majority of (SP) proteins has more than 1 research group asociatedThe majority of (SP) proteins has more than 1 research group asociated
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So…..can we use wikis for this ??????So…..can we use wikis for this ??????
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• Contextual annotation of web pages for interactive browsing, van Mulligen E, Diwersy M, Schijvenaars B, Weeber M, van der Eijk CC, Jelier R, Schuemie M, Kors J, Mons B, Medinfo 2004, 11:94-8• Which gene did you mean?, Mons B, BMC Bioinformatics 2005 Jun 7, 6:142
First order semantic enrichment
The Knowlet
2nd order S.E.
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<Source concept><Target Concept><Relations>:
<Typea1> Database facts (mutiple attributes)<Typea2> Community Annotations (WikiProf)<Typeb1> Co-occurrence sentence<Typeb2> Co-occurrence abstract<Typec1> Concept Profile Match<Type c2> Sequence similarity (BLAST score Genes and Proteins only)<Type c3 Co-expression with (genes from expression Databases)
Knowlet building block
Knowlet of core concept Knowlet space
What does a Knowlet look like ‘under the hood’?
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KKKK DDDD
KKKK GGGG
KKKK HHHH
KKKK ZZZZ
KKKK BBBB
KKKK EEEE
KKKK AAAA
KKKK DDDD
KKKK FFFF
KKKK IIII
KKKK DDDD
KKKK GGGG
KKKK HHHH
KKKK ZZZZ
KKKK BBBB
factual associativeco occurrence
• Rules to combine different sources of information into a single relationship
• Time-stamped information• The relationship to the original texts or
database entries
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• A Knowlet represents a unit of thought interconnected with other units of thoughts or in other words: a ‘cloud’ of concepts that have one or more relationship types with the central (selected) concept
• The interconnection reflects a semantic relationship derived:
– From facts in database– From co-occurrence in a text– From other associations
• Relations have a strength– Based on the source of relationship– Based on the amount of «evidence»
• Knowlets belong to one or more semantic classes: proteins, diseases, authors, organizations, journals, experiments, etc.
• Each Knowlet is uniquely identified by a URL or URI (Unique Resource Identifier)
The Knowlet
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person organisation Object 1
gene
Object 2
disease
Object 3
drug
3. Building an association matrix of large data sources
1 Million
1 Million
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Function unknow nChaperonesChromatin structureFibrous proteinsmRNA metabolismOthersRibosomal proteinsRibosome biogenesisTranslation
SRPPARN
l
• Assignment of protein function and discovery of new nucleolar proteins based on automatic analysis of MEDLINE. Martijn Schuemie, Christine Chichester, Frederique Lisaceck, Yohann Coute, Peter-Jan Roes, Jean Charles Sanchez, Barend MonsSpecial issue on Systems Biology in Proteomics, 2008 (accepted for publication)
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Cluster studies on basis of Homologene IDs
Cluster 4: EOM-specific genes in mdx
Cluster 5: Development of EOM muscleand rat atrophy
Cluster 1:Mdx miceDysferlin-deficient mice
Cluster 2: myositisCluster 3: DMD
GeneSet Clusterer, Rob Jelier, Erasmus MC
Kappa-based clustering based on Gene ID
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Clustering of genes based on similarity of concept profiles
GeneSet Clusterer, Rob Jelier, Erasmus MC
Cluster 1: atrophy and myopathy
Cluster 2: extraocular muscle of mdx
Cluster 3: human and mouse muscular dystrophies and myositis
Cluster 4: long gene lists
Cluster 5: muscle differentiation;Ky-mutant and Fxr-/- mice
Cluster 6: ageing and sarcopenia
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Evaluate biological processes that bring studies together
DatasetComparer, Rob Jelier, Erasmus MC
No overlap on GeneID level
Many assocations on concept profile level
Annotate
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• OmegaWiki (terminology system)• Wiki Authors• Wiki Medical/Clinical• Wiki Proteins• Wiki Chemicals• Wiki Etc.
Allow for:
• Community Annotation• Quick growth of terminology systems• Semantic Linking between concepts
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Knowlet
Association Matrix Meta-analysis
Expert Challenge
WikiZ/PExpert comments
Peer to Peer Review
Final Approval
U.W. Fingerprint
Update
Literature
Protein A
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0.1
0.4
0.9
New publications or annotations
Solid (a)
Liquid (b)
Gas (C)
1st order Semantic enrichment
ReductionFalse Positives
DiscussionVoting in Wiki
Meta-analysisProximity measures
Proposals to Data bases ?
Central Annotation
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• REGISTRATION (1X)
• Unique Author ID• E-mail Adress• PHP/userpage• People Knowlets
• Unique concept ID• Language variants• Homonyms• Definitions (brief)• Object Knowlets
Science Wiki’s• UID from WiktionaryZ• Research information• Talk-page• Liquid Threads• Object Knowlets
• UID from WiktionaryZ• Articles about UID’s• Encyclopaedic/ NPOV• Anonymous allowed
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Nature News February 15, 2007
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v
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Step 2: select concepts of interest
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Cellular Memberan (GO)Mosquitoes
Core concept: Malaria (mean distance 5)
Semantic distance to: Malaria
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Plasmodium Chabaudi
F+, C+, A+
F-, C+, A +
F-, C-, A+
The Knowlet Visualization
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v chloroquineprimaquineNew Drug ????
Mosquitoes
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Core concept: Malaria (mean distance 5)
Para-amino-benzoic acidCellular Memberan (GO)