Affiliation:
1. School of Computer Science and Engineering, Changsha University, Changsha, 410022, China
2. Key Laboratory of
Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, China
Abstract
Background:
Human microbial communities play an important role in some physiological
process of human beings. Nevertheless, the identification of microbe-disease associations through biological
experiments is costly and time-consuming. Hence, the development of calculation models is
meaningful to infer latent associations between microbes and diseases.
Aims:
In this manuscript, we aim to design a computational model based on the Graph Convolutional
Neural Network with Multi-layer Attention mechanism, called GCNMA, to infer latent microbe-disease
associations.
Objective:
This study aims to propose a novel computational model based on the Graph Convolutional
Neural Network with Multi-layer Attention mechanism, called GCNMA, to detect potential microbedisease
associations.
Methods:
In GCNMA, the known microbe-disease association network was first integrated with the microbe-
microbe similarity network and the disease-disease similarity network into a heterogeneous network
first. Subsequently, the graph convolutional neural network was implemented to extract embedding
features of each layer for microbes and diseases respectively. Thereafter, these embedding features
of each layer were fused together by adopting the multi-layer attention mechanism derived from the
graph convolutional neural network, based on which, a bilinear decoder would be further utilized to infer
possible associations between microbes and diseases.
Results:
Finally, to evaluate the predictive ability of GCNMA, intensive experiments were done and
compared results with eight state-of-the-art methods which demonstrated that under the frameworks of
both 2-fold cross-validations and 5-fold cross-validations, GCNMA can achieve satisfactory prediction
performance based on different databases including HMDAD and Disbiome simultaneously. Moreover,
case studies on three kinds of common diseases such as asthma, type 2 diabetes, and inflammatory bowel
disease verified the effectiveness of GCNMA as well.
Conclusion:
GCNMA outperformed 8 state-of-the-art competitive methods based on the benchmarks of
both HMDAD and Disbiome.
Funder
National Natural Science Foundation of China
Key Project of Changsha Science and Technology Plan
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
1 articles.
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