Representation learning of drug and disease terms for drug repositioning

TL;DR

This paper introduces a novel method for drug repositioning that combines text-based and structured data to learn representations of drugs and diseases, improving prediction of new drug-disease associations.

Contribution

It proposes a new representation learning approach integrating unstructured texts and structured data, and applies matrix completion for drug-disease association prediction.

Findings

Competitive performance with state-of-the-art methods

Case studies on Alzheimer's and Hypertension validate predictions

Utilizes both free texts and structured datasets for improved accuracy

Abstract

Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug repositioning. DR exploits two major aspects associated with drugs and diseases: existence of similarity among drugs and among diseases due to their shared involved genes or pathways or common biological effects. Existing methods of identifying drug-disease association majorly rely on the information available in the structured databases only. On the other hand, abundant information available in form of free texts in biomedical research articles are not being fully exploited. Word-embedding or obtaining vector representation of words from a large corpora of free texts using neural network methods have been shown to give significant performance for several natural language processing tasks. In this work we propose a novel way of representation learning to obtain features of drugs and diseases by combining complementary information available in unstructured texts and structured datasets. Next we use matrix completion approach on these feature vectors to learn projection matrix between drug and disease vector spaces. The proposed method has shown competitive performance with state-of-the-art methods. Further, the case studies on Alzheimer's and Hypertension diseases have shown that the predicted associations are matching with the existing knowledge.