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Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization

The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and cos...

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Autores principales: Ha, Jihwan, Park, Chihyun, Park, Chanyoung, Park, Sanghyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226829/
https://www.ncbi.nlm.nih.gov/pubmed/32260218
http://dx.doi.org/10.3390/cells9040881
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author Ha, Jihwan
Park, Chihyun
Park, Chanyoung
Park, Sanghyun
author_facet Ha, Jihwan
Park, Chihyun
Park, Chanyoung
Park, Sanghyun
author_sort Ha, Jihwan
collection PubMed
description The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework—improved prediction of miRNA-disease associations (IMDN)—based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates.
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spelling pubmed-72268292020-05-18 Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization Ha, Jihwan Park, Chihyun Park, Chanyoung Park, Sanghyun Cells Article The identification of potential microRNA (miRNA)-disease associations enables the elucidation of the pathogenesis of complex human diseases owing to the crucial role of miRNAs in various biologic processes and it yields insights into novel prognostic markers. In the consideration of the time and costs involved in wet experiments, computational models for finding novel miRNA-disease associations would be a great alternative. However, computational models, to date, are biased towards known miRNA-disease associations; this is not suitable for rare miRNAs (i.e., miRNAs with a few known disease associations) and uncommon diseases (i.e., diseases with a few known miRNA associations). This leads to poor prediction accuracies. The most straightforward way of improving the performance is by increasing the number of known miRNA-disease associations. However, due to lack of information, increasing attention has been paid to developing computational models that can handle insufficient data via a technical approach. In this paper, we present a general framework—improved prediction of miRNA-disease associations (IMDN)—based on matrix completion with network regularization to discover potential disease-related miRNAs. The success of adopting matrix factorization is demonstrated by its excellent performance in recommender systems. This approach considers a miRNA network as additional implicit feedback and makes predictions for disease associations relevant to a given miRNA based on its direct neighbors. Our experimental results demonstrate that IMDN achieved excellent performance with reliable area under the receiver operating characteristic (ROC) area under the curve (AUC) values of 0.9162 and 0.8965 in the frameworks of global and local leave-one-out cross-validations (LOOCV), respectively. Further, case studies demonstrated that our method can not only validate true miRNA-disease associations but also suggest novel disease-related miRNA candidates. MDPI 2020-04-03 /pmc/articles/PMC7226829/ /pubmed/32260218 http://dx.doi.org/10.3390/cells9040881 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ha, Jihwan
Park, Chihyun
Park, Chanyoung
Park, Sanghyun
Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization
title Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization
title_full Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization
title_fullStr Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization
title_full_unstemmed Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization
title_short Improved Prediction of miRNA-Disease Associations Based on Matrix Completion with Network Regularization
title_sort improved prediction of mirna-disease associations based on matrix completion with network regularization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7226829/
https://www.ncbi.nlm.nih.gov/pubmed/32260218
http://dx.doi.org/10.3390/cells9040881
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