Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms.
J Cell Mol Med
; 28(17): e18553, 2024 Sep.
Article
en En
| MEDLINE
| ID: mdl-39239860
ABSTRACT
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 L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and the L 2 , 1 $$ {L}_{2,1} $$ 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.
Palabras clave
graph dual regularization <ns0:math> <semantics> <mrow> <msub> <mi>L</mi> <mrow> <mn>2</mn> <mo>,</mo> <mn>1</mn> </mrow> </msub> </mrow> <annotation>$$ {L}_{2,1} $$</annotation> </semantics> </math> norm regularization; inertial proximal alternating linearized minimization; microbedisease association; nonnegative matrix factorization
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1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
Límite:
Humans
Idioma:
En
Revista:
J Cell Mol Med
Asunto de la revista:
BIOLOGIA MOLECULAR
Año:
2024
Tipo del documento:
Article
País de afiliación:
China