Revealing new therapeutic opportunities through drug target prediction: a class imbalance-tolerant machine learning approach.

ABSTRACT:

Motivation

In silico drug target prediction provides valuable information for drug repurposing, understanding of side effects as well as expansion of the druggable genome. In particular, discovery of actionable drug targets is critical to developing targeted therapies for diseases.

Results

Here, we develop a robust method for drug target prediction by leveraging a class imbalance-tolerant machine learning framework with a novel training scheme. We incorporate novel features, including drug-gene phenotype similarity and gene expression profile similarity that capture information orthogonal to other features. We show that our classifier achieves robust performance and is able to predict gene targets for new drugs as well as drugs that potentially target unexplored genes. By pr

SUBMITTER: Liang S 

PROVIDER: S-EPMC7750999 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature