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

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Liu, Xianzhi*;Liang, Mingmin;Yu, Ge;Tang, Shichang;Wu, Ouxiang;...

通讯作者：

Liu, Xianzhi;Wu, OX;Liang, MM

作者机构：

[Wu, Ouxiang; Liu, Xianzhi; Zeng, Bin] Hunan Vocat Coll Elect & Technol, Sch Informat Engn, Changsha, Peoples R China.

[Liang, Mingmin; Yu, Ge] Hunan Vocat Coll Sci & Technol, Sch Intelligent Equipment Technol, Changsha, Peoples R China.

[Tang, Shichang] Cent South Univ Forestry & Technol, Sch Continuing Educ, Changsha, Peoples R China.

[Wang, Lei] Changsha Univ, Big Data Innovat & Entrepreneurship Educ Ctr Hunan, Changsha, Peoples R China.

通讯机构：

[Liang, MM ; Liu, XZ; Wu, OX ] H

Hunan Vocat Coll Elect & Technol, Sch Informat Engn, Changsha, Peoples R China.

Hunan Vocat Coll Sci & Technol, Sch Intelligent Equipment Technol, Changsha, Peoples R 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：

页码：

1618472

DOI：

10.3389/fgene.2025.1618472

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...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

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

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问