A novel microbe-drug association prediction model based on stacked autoencoder with multi-head attention mechanism

Abstract

Microbes are intimately tied to the occurrence of various diseases that cause serious hazards to human health, and play an essential role in drug discovery, clinical application, and drug quality control. In this manuscript, we put forward a novel prediction model named MDASAE based on a stacked autoencoder (SAE) with multi-head attention mechanism to infer potential microbe-drug associations. In MDASAE, we first constructed three kinds of microbe-related and drug-related similarity matrices based on known microbe-disease-drug associations respectively. And then, we fed two kinds of microbe-related and drug-related similarity matrices respectively into the SAE to learn node attribute features, and introduced a multi-head attention mechanism into the output layer of the SAE to enhance feature extraction. Thereafter, we further adopted the remaining microbe and drug similarity matrices to derive inter-node features by using the Restart Random Walk algorithm. After that, the node attribute features and inter-node features of microbes and drugs would be fused together to predict scores of possible associations between microbes and drugs. Finally, intensive comparison experiments and case studies based on different well-known public databases under 5-fold cross-validation and 10-fold cross-validation respectively, proved that MDASAE can effectively predict the potential microbe-drug associations.

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

“…To validate the predictive performance of GARFMDA, we will compare it with the following five representative approaches separately: LAGCN [ 20 ]: which is a computational model for inferring unknown drug-disease associations based on graph convolutional networks and attention mechanisms GSAMDA [ 21 ]: which is a microbe-drug association prediction model based on graph attention networks and sparse autoencoders SCSMDA [ 22 ]: which aims to predict microbe-drug associations based on the structure-enhanced contrast learning and self-paced negative sampling strategies. MDASAE [ 23 ]: which is a calculation method based on fusing multi-attention mechanisms with stacked autoencoders to detect possible microbial drug associations. LRLSHMDA [ 24 ]: which is a computational scheme by exploiting Laplace Regularised Least Squares to predict microbe-disease associations.…”

“…MDASAE [ 23 ]: which is a calculation method based on fusing multi-attention mechanisms with stacked autoencoders to detect possible microbial drug associations.…”

LCASPMDA: a computational model for predicting potential microbe-drug associations based on learnable graph convolutional attention networks and self-paced iterative sampling ensemble

“…MDASAEA ( Fan et al, 2023 ): which predicted latent microbe-drug associations by combining the self-sparse encoders and the multi-head attention networks.…”