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BANSMDA: a computational model for predicting potential microbe-disease associations based on bilinear attention networks and sparse autoencoders.

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成果类型:
期刊论文
作者:
Liu, Xianzhi;Liang, Mingmin;Yu, Ge;Tang, Shichang;Wu, Ouxiang;...
作者机构:
[Wu, Ouxiang; Liu, Xianzhi; Zeng, Bin] School of Information Engineering, Hunan Vocational College of Electronic and Technology, Changsha, China
[Liang, Mingmin; Yu, Ge] School of Intelligent Equipment, Hunan Vocational College of Electronic and Technology, Changsha, China
[Tang, Shichang] School of Continuing Education, Central South University of Forestry and Technology, Changsha, China
[Wang, Lei] Big Data Innovation and Entrepreneurship Education Center of Hunan Province, Changsha University, Changsha, China
语种:
英文
关键词:
bilinear attention networks;computational model;microbe-disease associations;prediction;sparse autoencoder
期刊:
Frontiers in Genetics
ISSN:
1664-8021
年:
2025
卷:
16
页码:
1618472
基金类别:
The author(s) declare that financial support was received for the research and/or publication of this article. This work was partly sponsored by the National Natural Science Foundation of China (No. 62272064), the Natural Science Foundation of Hunan Province (No. 2023JJ60185 and No. 2025JJ90184), the Scientific Research Project of Hunan Provincial Department of Education (No. 23C0543 and No. 23C0544), and the Key project of Changsha Science and technology Plan (No. KQ2203001).
机构署名:
本校为其他机构
院系归属:
继续教育学院
摘要:
INTRODUCTION: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ecological health, and drug development. METHODS: In this manuscript, we introduce an innovative computational model named BANSMDA, which integrates Bilinear Attention Networks with sparse autoencoder to uncover hidden connections between microbes and diseases. In BANSMDA, we first constructed a heterogeneous microbe-disease network by integrating multiple Gaussian similarity measures for diseases and mi...

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