Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1785032367786688512 |
---|---|
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. |
format | Online Article Text |
id | pubmed-10137058 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT wangshudong amcsmmapredictingsmallmoleculemirnapotentialassociationsbasedonaccuratematrixcompletion AT renchuanru amcsmmapredictingsmallmoleculemirnapotentialassociationsbasedonaccuratematrixcompletion AT zhangyulin amcsmmapredictingsmallmoleculemirnapotentialassociationsbasedonaccuratematrixcompletion AT pangshanchen amcsmmapredictingsmallmoleculemirnapotentialassociationsbasedonaccuratematrixcompletion AT qiaosibo amcsmmapredictingsmallmoleculemirnapotentialassociationsbasedonaccuratematrixcompletion AT wuwenhao amcsmmapredictingsmallmoleculemirnapotentialassociationsbasedonaccuratematrixcompletion AT linboyang amcsmmapredictingsmallmoleculemirnapotentialassociationsbasedonaccuratematrixcompletion |