Drug target inference by mining transcriptional data using a novel graph convolutional network framework.

ABSTRACT: A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situatio

SUBMITTER: Zhong F 

PROVIDER: S-EPMC8532448 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature