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