Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms

Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and...

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Bibliographic Details
Published in:Journal of cellular and molecular medicine Vol. 28; no. 17; p. e18553
Main Authors: Chen, Ziwei, Zhang, Liangzhe, Li, Jingyi, Chen, Hang
Format: Journal Article
Language:English
Published: England 01.09.2024
Subjects:
Algorithms
Computational Biology - methods
Humans
Microbiota
graph dual regularization
microbe–disease association
inertial proximal alternating linearized minimization
norm regularization
non‐negative matrix factorization
ISSN:1582-4934, 1582-4934
Online Access:Get full text
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Summary:Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this study, we introduced a new method called iPALM-GLMF, which modelled microbe-disease association prediction as a problem of non-negative matrix factorization with graph dual regularization terms and norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and the norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non-negative matrix factorization and to improve the interpretability. To solve the model, iPALM-GLMF used a non-negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM-GLMF performed better than other existing methods in leave-one-out cross-validation and fivefold cross-validation. In addition, case studies of different diseases demonstrated that iPALM-GLMF could effectively predict potential microbial-disease associations. iPALM-GLMF is publicly available at https://github.com/LiangzheZhang/iPALM-GLMF.
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ISSN:1582-4934
1582-4934
DOI:10.1111/jcmm.18553