LLCMDA: A Novel Method for Predicting miRNA Gene and Disease Relationship Based on Locality-Constrained Linear Coding

MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to...

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Veröffentlicht in:Frontiers in genetics Jg. 9; S. 576
Hauptverfasser: Qu, Yu, Zhang, Huaxiang, Lyu, Chen, Liang, Cheng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Media S.A 28.11.2018
Schlagworte:
association prediction
Genetics
label propagation
locality-constrained linear coding
miRNA gene–disease relationship
similarity measure
similarity measure
label propagation
miRNA gene–disease relationship
association prediction
locality-constrained linear coding
ISSN:1664-8021, 1664-8021
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Zusammenfassung:MiRNAs are small non-coding regulatory RNAs which are associated with multiple diseases. Increasing evidence has shown that miRNAs play important roles in various biological and physiological processes. Therefore, the identification of potential miRNA-disease associations could provide new clues to understanding the mechanism of pathogenesis. Although many traditional methods have been successfully applied to discover part of the associations, they are in general time-consuming and expensive. Consequently, computational-based methods are urgently needed to predict the potential miRNA-disease associations in a more efficient and resources-saving way. In this paper, we propose a novel method to predict miRNA-disease associations based on Locality-constrained Linear Coding (LLC). Specifically, we first reconstruct similarity networks for both miRNAs and diseases using LLC and then apply label propagation on the similarity networks to get relevant scores. To comprehensively verify the performance of the proposed method, we compare our method with several state-of-the-art methods under different evaluation metrics. Moreover, two types of case studies conducted on two common diseases further demonstrate the validity and utility of our method. Extensive experimental results indicate that our method can effectively predict potential associations between miRNAs and diseases.
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Edited by: Quan Zou, Tianjin University, China
This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics
Reviewed by: Zhenjia Wang, University of Virginia, United States; Xiangxiang Zeng, Xiamen University, China
ISSN:1664-8021
1664-8021
DOI:10.3389/fgene.2018.00576