Predicting human microbe-drug associations via graph convolutional network with conditional random field.

ABSTRACT:

Motivation

Human microbes play critical roles in drug development and precision medicine. How to systematically understand the complex interaction mechanism between human microbes and drugs remains a challenge nowadays. Identifying microbe-drug associations can not only provide great insights into understanding the mechanism, but also boost the development of drug discovery and repurposing. Considering the high cost and risk of biological experiments, the computational approach is an alternative choice. However, at present, few computational approaches have been developed to tackle this task.

Results

In this work, we leveraged rich biological information to construct a heterogeneous network for drugs and microbes, including a microbe similarity network, a drug similarity net

SUBMITTER: Long Y 

PROVIDER: S-EPMC7559035 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature