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SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction

MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Predicti...

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Autores principales: Li, Lei, Gao, Zhen, Zheng, Chun-Hou, Wang, Yu, Wang, Yu-Tian, Ni, Jian-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900415/
https://www.ncbi.nlm.nih.gov/pubmed/33634120
http://dx.doi.org/10.3389/fcell.2021.617569
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author Li, Lei
Gao, Zhen
Zheng, Chun-Hou
Wang, Yu
Wang, Yu-Tian
Ni, Jian-Cheng
author_facet Li, Lei
Gao, Zhen
Zheng, Chun-Hou
Wang, Yu
Wang, Yu-Tian
Ni, Jian-Cheng
author_sort Li, Lei
collection PubMed
description MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA–disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA–disease.
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spelling pubmed-79004152021-02-24 SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction Li, Lei Gao, Zhen Zheng, Chun-Hou Wang, Yu Wang, Yu-Tian Ni, Jian-Cheng Front Cell Dev Biol Cell and Developmental Biology MicroRNAs (miRNAs) that belong to non-coding RNAs are verified to be closely associated with several complicated biological processes and human diseases. In this study, we proposed a novel model that was Similarity Network Fusion and Inductive Matrix Completion for miRNA-Disease Association Prediction (SNFIMCMDA). We applied inductive matrix completion (IMC) method to acquire possible associations between miRNAs and diseases, which also could obtain corresponding correlation scores. IMC was performed based on the verified connections of miRNA–disease, miRNA similarity, and disease similarity. In addition, miRNA similarity and disease similarity were calculated by similarity network fusion, which could masterly integrate multiple data types to obtain target data. We integrated miRNA functional similarity and Gaussian interaction profile kernel similarity by similarity network fusion to obtain miRNA similarity. Similarly, disease similarity was integrated in this way. To indicate the utility and effectiveness of SNFIMCMDA, we both applied global leave-one-out cross-validation and five-fold cross-validation to validate our model. Furthermore, case studies on three significant human diseases were also implemented to prove the effectiveness of SNFIMCMDA. The results demonstrated that SNFIMCMDA was effective for prediction of possible associations of miRNA–disease. Frontiers Media S.A. 2021-02-09 /pmc/articles/PMC7900415/ /pubmed/33634120 http://dx.doi.org/10.3389/fcell.2021.617569 Text en Copyright © 2021 Li, Gao, Zheng, Wang, Wang and Ni. 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 Cell and Developmental Biology
Li, Lei
Gao, Zhen
Zheng, Chun-Hou
Wang, Yu
Wang, Yu-Tian
Ni, Jian-Cheng
SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction
title SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction
title_full SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction
title_fullStr SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction
title_full_unstemmed SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction
title_short SNFIMCMDA: Similarity Network Fusion and Inductive Matrix Completion for miRNA–Disease Association Prediction
title_sort snfimcmda: similarity network fusion and inductive matrix completion for mirna–disease association prediction
topic Cell and Developmental Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7900415/
https://www.ncbi.nlm.nih.gov/pubmed/33634120
http://dx.doi.org/10.3389/fcell.2021.617569
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