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

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, we constructed a unique feature matrix for drugs and microbes respectively. Next, we fed these newly-obtained microbe-drug networks together with feature matrices into the graph attention network to extract the low-dimensional feature representations for drugs and microbes separately. Thereafter, these low-dimensional feature representations, along with the feature matrices, would be further inputted into the first layer of the Bilayer random forest model to obtain the contribution values of all features. And then, after removing features with low contribution values, these contribution values would be fed into the second layer of the Bilayer random forest to detect potential links between microbes and drugs.

CONCLUSIONS: Experimental results and case studies show that GARFMDA can achieve better prediction performance than state-of-the-art approaches, which means that GARFMDA may be a useful tool in the field of microbe-drug association prediction in the future. Besides, the source code of GARFMDA is available at https://github.com/KuangHaiYue/GARFMDA.git.