This document proposes a method to infer hidden relationships between biological entities like genes and diseases by analyzing literature. It describes constructing context term vectors from literature about Alzheimer's disease to identify drug-disease interactions. These inferred interactions are then evaluated by comparing to known interactions from an existing ABC model, with the goal of improving on the ABC model's ability to find interactions requiring manual input from experts.

1. Infer Hidden Relationships From Literature
By Multi-Level Context Terms

2. 1. Why?
2. How?
3. Evaluate Result
Table of Contents

3. Why

4. ABC Model

5. •Has many candidates
•Is semi-automatic
•Requires expert’s manual input.
ABC Model’s Disadvantage

6. How
Let’s infer drugs that interact with Alzheimer’s disease

7. 1. Construct Biological Data Entities
2. Collect Literatures about Alzheimer’s disease
3. Extract Interactions with Context Term Vector
4. Infer Undiscovered Interactions

8. Biological Data Entities
Gene, Drug, Disease, Symptom, Protein, Molecule, Process, Disease
We need databases of

9. Download Literatures from PubMed

10. How to Extract Interaction
1. Make A-B and B-C context vector
2. Calculate A-B-C similarity score

11. Make Context Vector

12. Caculate Similarity Score
•Cosine similarity
•Spearman Correlation

13. Infer Undiscovered Interactions
•Sum of all A-B-C scores
•Maximum of A-B-C scores
•Count of some A-B-C scores ( > threshold)

14. Evaluate Result
by Comparing with ABC model

15. 1. Top 100, 500, 1000 Interactions
2.Top 10 Interactions

16. Precision for ABC Model VS Similarity

17. CTD-Cosine-Hybrid
CTD-ABC Model