Predicting Microbe-Metabolite Interactions by Integrating Non-negative Matrix Factorization and Generative Network

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成果类型：

会议论文

作者：

Yi Jia;Shanshan Zheng;Tingting He;Xingpeng Jiang

作者机构：

School of Mathematics and Statistics, Central China Normal University, WuHan, PR China

Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, WuHan, PR China

School of Computer, Central China Normal University, WuHan, PR China

National Language Resources Monitoring & Research Center for Network Media, Central China Normal University, WuHan, PR China

[Yi Jia] School of Mathematics and Statistics, Central China Normal University, WuHan, PR China<&wdkj&>Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning, Central China Normal University, WuHan, PR China<&wdkj&>School of Computer, Central China Normal University, WuHan, PR China

语种：

英文

关键词：

Non-negative matrix factorization;Microbe-metabolite interactions;Multi-view;Microbiome;Metabolome

年：

2023

页码：

165-170

会议名称：

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

会议论文集名称：

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

会议时间：

05 December 2023

会议地点：

Istanbul, Turkiye

出版者：

IEEE

ISBN：

979-8-3503-3749-5

DOI：

10.1109/BIBM58861.2023.10385992

基金类别：

10.13039/501100001809-National Natural Science Foundation of China 10.13039/501100003819-Natural Science Foundation of Hubei Province

机构署名：

本校为第一机构

院系归属：

数学与统计学学院

计算机学院

摘要：

Despite profound impacts on human health and nature, accurately predicting microbe-metabolite interactions remains challenging due to inherent data noise. This study applies non-negative matrix factorization (NMF) and multi-view NMF to reduce noise and exploit associations across data perspectives. NMF obtains low-dimensional microbial and metabolic representations, effectively reducing noise. The dimension-reduced spectral matrices were input into the generative network model to derive conditional probabilities of individual microbe-associated metabolites and microbe-metabolite co-occurrence ...

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Predicting Microbe-Metabolite Interactions by Integrating Non-negative Matrix Factorization and Generative Network

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