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