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AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion

Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on accurate matrix completion for predicting pot...

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Detalles Bibliográficos
Autores principales: Wang, Shudong, Ren, Chuanru, Zhang, Yulin, Pang, Shanchen, Qiao, Sibo, Wu, Wenhao, Lin, Boyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137058/
https://www.ncbi.nlm.nih.gov/pubmed/37190032
http://dx.doi.org/10.3390/cells12081123
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author Wang, Shudong
Ren, Chuanru
Zhang, Yulin
Pang, Shanchen
Qiao, Sibo
Wu, Wenhao
Lin, Boyang
author_facet Wang, Shudong
Ren, Chuanru
Zhang, Yulin
Pang, Shanchen
Qiao, Sibo
Wu, Wenhao
Lin, Boyang
author_sort Wang, Shudong
collection PubMed
description Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on accurate matrix completion for predicting potential SM–miRNA associations (AMCSMMA). Initially, a heterogeneous SM–miRNA network is constructed, and its adjacency matrix is taken as the target matrix. An optimization framework is then proposed to recover the target matrix with the missing values by minimizing its truncated nuclear norm, an accurate, robust, and efficient approximation to the rank function. Finally, we design an effective two-step iterative algorithm to solve the optimization problem and obtain the prediction scores. After determining the optimal parameters, we conduct four kinds of cross-validation experiments based on two datasets, and the results demonstrate that AMCSMMA is superior to the state-of-the-art methods. In addition, we implement another validation experiment, in which more evaluation metrics in addition to the AUC are introduced and finally achieve great results. In two types of case studies, a large number of SM–miRNA pairs with high predictive scores are confirmed by the published experimental literature. In summary, AMCSMMA has superior performance in predicting potential SM–miRNA associations, which can provide guidance for biological experiments and accelerate the discovery of new SM–miRNA associations.
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spelling pubmed-101370582023-04-28 AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion Wang, Shudong Ren, Chuanru Zhang, Yulin Pang, Shanchen Qiao, Sibo Wu, Wenhao Lin, Boyang Cells Article Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on accurate matrix completion for predicting potential SM–miRNA associations (AMCSMMA). Initially, a heterogeneous SM–miRNA network is constructed, and its adjacency matrix is taken as the target matrix. An optimization framework is then proposed to recover the target matrix with the missing values by minimizing its truncated nuclear norm, an accurate, robust, and efficient approximation to the rank function. Finally, we design an effective two-step iterative algorithm to solve the optimization problem and obtain the prediction scores. After determining the optimal parameters, we conduct four kinds of cross-validation experiments based on two datasets, and the results demonstrate that AMCSMMA is superior to the state-of-the-art methods. In addition, we implement another validation experiment, in which more evaluation metrics in addition to the AUC are introduced and finally achieve great results. In two types of case studies, a large number of SM–miRNA pairs with high predictive scores are confirmed by the published experimental literature. In summary, AMCSMMA has superior performance in predicting potential SM–miRNA associations, which can provide guidance for biological experiments and accelerate the discovery of new SM–miRNA associations. MDPI 2023-04-10 /pmc/articles/PMC10137058/ /pubmed/37190032 http://dx.doi.org/10.3390/cells12081123 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Shudong
Ren, Chuanru
Zhang, Yulin
Pang, Shanchen
Qiao, Sibo
Wu, Wenhao
Lin, Boyang
AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion
title AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion
title_full AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion
title_fullStr AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion
title_full_unstemmed AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion
title_short AMCSMMA: Predicting Small Molecule–miRNA Potential Associations Based on Accurate Matrix Completion
title_sort amcsmma: predicting small molecule–mirna potential associations based on accurate matrix completion
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137058/
https://www.ncbi.nlm.nih.gov/pubmed/37190032
http://dx.doi.org/10.3390/cells12081123
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