Hessian Regularized [Formula: see text]-Nonnegative Matrix Factorization and Deep Learning for miRNA-Disease Associations Prediction.
Interdiscip Sci
; 16(1): 176-191, 2024 Mar.
Article
en En
| MEDLINE
| ID: mdl-38099958
ABSTRACT
Since the identification of microRNAs (miRNAs), empirical research has demonstrated their crucial involvement in the functioning of organisms. Investigating miRNAs significantly bolsters efforts related to averting, diagnosing, and treating intricate human maladies. Yet, exploring every conceivable miRNA-disease association consumes significant resources and time within conventional wet experiments. On the computational front, forecasting potential miRNA-disease connections serves as a valuable source of preliminary insights for medical investigators. As a result, we have developed a novel matrix factorization model known as Hessian-regularized [Formula see text] nonnegative matrix factorization in combination with deep learning for predicting associations between miRNAs and diseases, denoted as [Formula see text]-NMF-DF. In particular, we introduce a novel iterative fusion approach to integrate all similarities. This method effectively diminishes the sparsity of the initial miRNA-disease associations matrix. Additionally, we devise a mixed model framework that utilizes deep learning, matrix decomposition, and singular value decomposition to capture and depict the intricate nonlinear features of miRNA and disease. The prediction performance of the six matrix factorization methods is improved by comparison and analysis, similarity matrix fusion, data preprocessing, and parameter adjustment. The AUC and AUPR obtained by the new matrix factorization model under fivefold cross validation are comparative or better with other matrix factorization models. Finally, we select three diseases including lung tumor, bladder tumor and breast tumor for case analysis, and further extend the matrix factorization model based on deep learning. The results show that the hybrid algorithm combining matrix factorization with deep learning proposed in this paper can predict miRNAs related to different diseases with high accuracy.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
MicroARNs
/
Aprendizaje Profundo
/
Neoplasias Pulmonares
Límite:
Humans
Idioma:
En
Revista:
Interdiscip Sci
Asunto de la revista:
BIOLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
China