# Predicting Drug Responses by Propagating Interactions through Text-Enhanced Drug-Gene Networks

## TL;DR

This paper introduces a method that combines biological literature and experimental data to construct a drug-gene interaction network, enabling explainable predictions of drug responses with high accuracy.

## Contribution

It presents a novel approach integrating text-mined interactions and experimental data to predict drug responses in a transparent manner.

## Key findings

- Achieved 94.74% accuracy in binary drug sensitivity prediction.
- Developed a white-box model for explainable drug response prediction.
- Constructed a drug-gene network from literature and experimental data.

## Abstract

Personalized drug response has received public awareness in recent years. How to combine gene test result and drug sensitivity records is regarded as essential in the real-world implementation. Research articles are good sources to train machine predicting, inference, reasoning, etc. In this project, we combine the patterns mined from biological research articles and categorical data to construct a drug-gene interaction network. Then we use the cell line experimental records on gene and drug sensitivity to estimate the edge embeddings in the network. Our model provides white-box explainable predictions of drug response based on gene records, which achieves 94.74% accuracy in binary drug sensitivity prediction task.