A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest.

ABSTRACT:

Background

In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experiments are very expensive and time-consuming. Therefore, it is meaningful to develop efficient computational models to forecast potential microbe-drug associations.

Results

In this manuscript, we proposed a novel prediction model called GARFMDA by combining graph attention networks and bilayer random forest to infer probable microbe-drug correlations. In GARFMDA, through integrating different microbe-drug-disease correlation indices, we constructed two different microbe-drug networks first. And then, based on multiple measures of similarity,

SUBMITTER: Kuang H 

PROVIDER: S-EPMC10877932 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature