infer hidden relationships from literature by multi level context terms
TRANSCRIPT
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Infer Hidden Relationships From LiteratureBy Multi-Level Context Terms
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1. Why?
2. How?
3. Evaluate Result
Table of Contents
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Why
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ABC Model
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•Has many candidates
•Is semi-automatic
•Requires expert’s manual input.
ABC Model’s Disadvantage
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HowLet’s infer drugs that interact with Alzheimer’s disease
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1. Construct Biological Data Entities
2. Collect Literatures about Alzheimer’s disease
3. Extract Interactions with Context Term Vector
4. Infer Undiscovered Interactions
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Biological Data Entities
Gene, Drug, Disease, Symptom, Protein, Molecule, Process, Disease
We need databases of
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Download Literatures from PubMed
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How to Extract Interaction
1. Make A-B and B-C context vector
2. Calculate A-B-C similarity score
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Make Context Vector
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Caculate Similarity Score
•Cosine similarity
•Spearman Correlation
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Infer Undiscovered Interactions
•Sum of all A-B-C scores
•Maximum of A-B-C scores
•Count of some A-B-C scores ( > threshold)
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Evaluate Resultby Comparing with ABC model
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1. Top 100, 500, 1000 Interactions
2.Top 10 Interactions
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Precision for ABC Model VS Similarity
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CTD-Cosine-Hybrid
CTD-ABC Model