Original Research ARTICLE
LLCMDA: A novel method for predicting miRNA gene and disease relationship based on Locality-constrained Linear Coding
- 1Shandong Normal University, China
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.
Keywords: miRNA gene-disease relationship, similarity measure, association prediction, Locality-constrained linear coding, Label propagation
Received: 14 Sep 2018;
Accepted: 08 Nov 2018.
Edited by:Quan Zou, University of Electronic Science and Technology of China, China
Reviewed by:Xiangxiang Zeng, Xiamen University, China
Zhenjia Wang, University of Virginia, United States
Copyright: © 2018 Qu, Zhang, Lyu and Liang. 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.
PhD. Xiang H. Zhang, Shandong Normal University, Jinan, China, firstname.lastname@example.org
PhD. Cheng Liang, Shandong Normal University, Jinan, China, email@example.com