Predicting metabolite–disease associations based on auto-encoder and non-negative matrix factorization

• Published in: Briefings in bioinformatics Vol. 24; no. 5
• Main Authors: Gao, Hongyan, Sun, Jianqiang, Wang, Yukun, Lu, Yuer, Liu, Liyu, Zhao, Qi, Shuai, Jianwei
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 20.09.2023; Oxford Publishing Limited (England)
• Subjects: Abnormalities; Chemical reactions; Coders; Deep learning; Disease; Factorization; Manpower; Metabolism; Metabolites; Modules; Multilayer perceptrons; Multilayers; auto-encoder; diseases; feature splicing; multi-layer perceptron; metabolites; non-negative matrix factorization
• DOI: 10.1093/bib/bbad259
• ISSN: 1467-5463; 1477-4054

Abstract Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite–disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision–recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.