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MGATMDA: Predicting microbe-disease associations via multi-component graph attention network
- Source :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics. :1-1
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Microbes are parasitic in various human body organs and play significant roles in a wide range of diseases. Identifying microbe-disease associations is conducive to the identification of potential drug targets. Considering the high cost and risk of biological experiments, developing computational approaches to explore the relationship between microbes and diseases is an alternative choice. However, most existing methods are based on unreliable or noisy similarity, and the prediction accuracy could be affected. Besides, it is still a great challenge for most previous methods to make predictions for the large-scale dataset. In this work, we develop a multi-component Graph Attention Network (GAT) based framework, termed MGATMDA, for predicting microbe-disease associations. MGATMDA is built on a bipartite graph of microbes and diseases. It contains three essential parts: decomposer, combiner, and predictor. The decomposer first decomposes the edges in the bipartite graph to identify the latent components by node-level attention mechanism. The combiner then recombines these latent components automatically to obtain unified embedding for prediction by component-level attention mechanism. Finally, a fully connected network is used to predict unknown microbes-disease associations. Experimental results showed that our proposed method outperformed eight state-of-the-art methods.
- Subjects :
- Computer science
business.industry
Mechanism (biology)
Applied Mathematics
Machine learning
computer.software_genre
Range (mathematics)
Identification (information)
Component (UML)
Similarity (psychology)
Genetics
Bipartite graph
Graph (abstract data type)
Embedding
Artificial intelligence
business
computer
Biotechnology
Subjects
Details
- ISSN :
- 23740043 and 15455963
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Accession number :
- edsair.doi.dedup.....745d4c6f9ec8d576986364f379f63525