SWnet: a deep learning model for drug response prediction from cancer genomic signatures and compound chemical structures.

ABSTRACT:

Background

One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model.

Results

We presented here a novel deep-learning model that integrates gene expression, genetic mutation, and chemical structure of compounds in a multi-task convolutional architecture. We applied our model to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. We selected relevant cancer-related genes based on oncology genetics databa

SUBMITTER: Zuo Z 

PROVIDER: S-EPMC8434731 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature