LRMCMDA: Predicting miRNA-Disease Association by Integrating Low-Rank Matrix Completion With miRNA and Disease Similarity Information

LRMCMDA: Predicting miRNA-Disease Association by Integrating Low-Rank Matrix Completion With miRNA and Disease Similarity Information

Identifying disease-related microRNAs (miRNAs) is crucial to understanding the etiology and pathogenesis of many diseases. However, existing computational methods are facing a few dilemmas such as lacking “negative samples” (i.e. confirmed unrelated miRNA-disease pairs). In thi...

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Bibliographic Details
Main Authors: Junlin Xu, Wen Zhu, Lijun Cai, Bo Liao, Yajie Meng, Ju Xiang, Dawei Yuan, Geng Tian, Jialiang Yang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
miRNA-disease association
low-rank matrix completion
receiver operating characteristic curve (ROC)
area under curve (AUC)
area under the precise recall curve (AUPR)
Online Access:https://ieeexplore.ieee.org/document/9078784/

Internet

https://ieeexplore.ieee.org/document/9078784/

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