Predicting human microbe–disease associations via graph attention networks with inductive matrix completion

Abstract

Motivation human microbes play a critical role in an extensive range of complex human diseases and become a new target in precision medicine. In silico methods of identifying microbe–disease associations not only can provide a deep insight into understanding the pathogenic mechanism of complex human diseases but also assist pharmacologists to screen candidate targets for drug development. However, the majority of existing approaches are based on linear models or label propagation, which suffers from limitations in capturing nonlinear associations between microbes and diseases. Besides, it is still a great challenge for most previous methods to make predictions for new diseases (or new microbes) with few or without any observed associations. Results in this work, we construct features for microbes and diseases by fully exploiting multiply sources of biomedical data, and then propose a novel deep learning framework of graph attention networks with inductive matrix completion for human microbe-disease association prediction, named GATMDA. To our knowledge, this is the first attempt to leverage graph attention networks for this important task. In particular, we develop an optimized graph attention network with talking-heads to learn representations for nodes (i.e. microbes and diseases). To focus on more important neighbours and filter out noises, we further design a bi-interaction aggregator to enforce representation aggregation of similar neighbours. In addition, we combine inductive matrix completion to reconstruct microbe-disease associations to capture the complicated associations between diseases and microbes. Comprehensive experiments on two data sets (i.e. HMDAD and Disbiome) demonstrated that our proposed model consistently outperformed baseline methods. Case studies on two diseases, i.e. asthma and inflammatory bowel disease, further confirmed the effectiveness of our proposed model of GATMDA. Availability python codes and data set are available at: https://github.com/yahuilong/GATMDA. Contact luojiawei@hnu.edu.cn.

CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network

“…The VGAELDA [ 40 ] integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. The GATMDA [ 41 ] using graph attention networks with inductive matrix completion for human microbe-disease associations prediction. After fivefold CV, the AUC values of GCMDR, AE-RF, GCNMDA, SIMCCDA, VGAELDA, GATMDA and CRPGCN are 0.6882, 0.8653, 0.7714, 0.8291, 0.5114, 0.9254, 0.9720, respectively.…”

Novel Collaborative Weighted Non-negative Matrix Factorization Improves Prediction of Disease-Associated Human Microbes

“…The benchmark microbe–disease dataset is very sparse; it is essential to weaken the effect caused by the sparse dataset and let known observed data provide more effective information. Effective methods are still scarce since most MDAs remain unknown ( Fan et al, 2019 ; Long et al, 2021 ). It is necessary to overcome or weaken these limitations and develop new computational methods to improve prediction performance.…”

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks

“…However, the input features of diseases and miRNAs were initialized randomly, which reduced the ability of GCN. Long et al ( 2021 ) developed a novel computational model to predict microbe-disease association (GATMDA). It firstly constructed the input features by integrating similarities of diseases and microbes, and a bipartite network of known microbe-disease associations.…”