Improved low-rank matrix recovery method for predicting miRNA-disease association

MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research. Considering limitation of existing computational...

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Published in:Scientific reports 2017-07, Vol.7 (1), p.6007-10, Article 6007
Main Authors: Peng, Li, Peng, Manman, Liao, Bo, Huang, Guohua, Liang, Wei, Li, Keqin
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Language:English
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631/114/1305
631/553/2695
Computer applications
Disease
Humanities and Social Sciences
miRNA
multidisciplinary
Science
Science (multidisciplinary)
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description MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related pathogenic mechanisms remain incompletely understood. Revealing the potential relationship between miRNAs and diseases is a critical problem in biomedical research. Considering limitation of existing computational approaches, we develop improved low-rank matrix recovery (ILRMR) for miRNA-disease association prediction. ILRMR is a global method that can simultaneously prioritize potential association for all diseases and does not require negative samples. ILRMR can also identify promising miRNAs for investigating diseases without any known related miRNA. By integrating miRNA-miRNA similarity information, disease-disease similarity information, and miRNA family information to matrix recovery, ILRMR performs better than other methods in cross validation and case studies.
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subjects 631/114/1305
631/553/2695
Computer applications
Disease
Humanities and Social Sciences
miRNA
multidisciplinary
Science
Science (multidisciplinary)
title Improved low-rank matrix recovery method for predicting miRNA-disease association
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