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QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion

Studies have shown that microRNAs (miRNAs) are closely associated with many human diseases, but we have not yet fully understand the role and potential molecular mechanisms of miRNAs in the process of disease development. However, ordinary biological experiments often require higher costs, and compu...

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Autores principales: Wang, Lin, Chen, Yaguang, Zhang, Naiqian, Chen, Wei, Zhang, Yusen, Gao, Rui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643770/
https://www.ncbi.nlm.nih.gov/pubmed/33193744
http://dx.doi.org/10.3389/fgene.2020.594796
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author Wang, Lin
Chen, Yaguang
Zhang, Naiqian
Chen, Wei
Zhang, Yusen
Gao, Rui
author_facet Wang, Lin
Chen, Yaguang
Zhang, Naiqian
Chen, Wei
Zhang, Yusen
Gao, Rui
author_sort Wang, Lin
collection PubMed
description Studies have shown that microRNAs (miRNAs) are closely associated with many human diseases, but we have not yet fully understand the role and potential molecular mechanisms of miRNAs in the process of disease development. However, ordinary biological experiments often require higher costs, and computational methods can be used to quickly and effectively predict the potential miRNA-disease association effect at a lower cost, and can be used as a useful reference for experimental methods. For miRNA-disease association prediction, we have proposed a new method called Matrix completion algorithm based on q-kernel information (QIMCMDA). We use fivefold cross-validation and leave-one-out cross-validation to prove the effectiveness of QIMCMDA. LOOCV shows that AUC can reach 0.9235, and its performance is significantly better than other commonly used technologies. In addition, we applied QIMCMDA to case studies of three human diseases, and the results show that our method performs well in inferring potential interaction between miRNAs and diseases. It is expected that QIMCMDA will become an excellent supplement in the field of biomedical research in the future.
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spelling pubmed-76437702020-11-13 QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion Wang, Lin Chen, Yaguang Zhang, Naiqian Chen, Wei Zhang, Yusen Gao, Rui Front Genet Genetics Studies have shown that microRNAs (miRNAs) are closely associated with many human diseases, but we have not yet fully understand the role and potential molecular mechanisms of miRNAs in the process of disease development. However, ordinary biological experiments often require higher costs, and computational methods can be used to quickly and effectively predict the potential miRNA-disease association effect at a lower cost, and can be used as a useful reference for experimental methods. For miRNA-disease association prediction, we have proposed a new method called Matrix completion algorithm based on q-kernel information (QIMCMDA). We use fivefold cross-validation and leave-one-out cross-validation to prove the effectiveness of QIMCMDA. LOOCV shows that AUC can reach 0.9235, and its performance is significantly better than other commonly used technologies. In addition, we applied QIMCMDA to case studies of three human diseases, and the results show that our method performs well in inferring potential interaction between miRNAs and diseases. It is expected that QIMCMDA will become an excellent supplement in the field of biomedical research in the future. Frontiers Media S.A. 2020-10-22 /pmc/articles/PMC7643770/ /pubmed/33193744 http://dx.doi.org/10.3389/fgene.2020.594796 Text en Copyright © 2020 Wang, Chen, Zhang, Chen, Zhang and Gao. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Wang, Lin
Chen, Yaguang
Zhang, Naiqian
Chen, Wei
Zhang, Yusen
Gao, Rui
QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion
title QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion
title_full QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion
title_fullStr QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion
title_full_unstemmed QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion
title_short QIMCMDA: MiRNA-Disease Association Prediction by q-Kernel Information and Matrix Completion
title_sort qimcmda: mirna-disease association prediction by q-kernel information and matrix completion
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643770/
https://www.ncbi.nlm.nih.gov/pubmed/33193744
http://dx.doi.org/10.3389/fgene.2020.594796
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