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...

Full description

Saved in:
Bibliographic Details
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
Format: Article
Language:English
Subjects:
631/114/1305
631/553/2695
Computer applications
Disease
Humanities and Social Sciences
miRNA
multidisciplinary
Science
Science (multidisciplinary)
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-017-06201-3