Multi‐network logistic matrix factorization for metabolite–disease interaction prediction. Issue 11 (26th April 2020)

• Record Type: Journal Article
• Authors: Ma, Yingjun; He, Tingting; Jiang, Xingpeng
• Is Part Of: FEBS letters. Volume 594:Issue 11(2020)
• Page Start: 1675
• Page End: 1684
• Publication Date: 2020-04-26
• Subjects: disease similarity -- Kernel neighborhood similarity -- logistic matrix factorization -- metabolite similarity -- metabolite–disease interaction; Biochemistry -- Periodicals; Biophysics -- Periodicals; Molecular biology -- Periodicals; Biochimie -- Périodiques; Biochemistry; Biophysics; Molecular biology; Periodicals; 572.05
• DOI: 10.1002/1873-3468.13782
• Languages: English
• ISSNs: 0014-5793

Abstract

Abstract : Identifying disease‐related metabolites is of great significance for the diagnosis, prevention, and treatment of disease. In this study, we propose a novel computational model of multiple‐network logistic matrix factorization (MN‐LMF) for predicting metabolite–disease interactions, which is especially relevant for new diseases and new metabolites. First, MN‐LMF builds disease (or metabolite) similarity network by integrating heterogeneous omics data. Second, it combines these similarities with known metabolite–disease interaction networks, using modified logistic matrix factorization to predict potential metabolite–disease interactions. Experimental results show that MN‐LMF accurately predicts metabolite–disease interactions, and outperforms other state‐of‐the‐art methods. Moreover, case studies also demonstrated the effectiveness of the model to infer unknown metabolite–disease interactions for novel diseases without any known associations. Abstract :