[1]LIU Xiaoyan,CHEN Xi,GUO Maozu,et al.A matrix factorization method for predicting miRNA-disease association[J].CAAI Transactions on Intelligent Systems,2018,13(6):897-904.[doi:10.11992/tis.201805043]
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A matrix factorization method for predicting miRNA-disease association

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 6 Page number: 897-904 Column: 学术论文—机器学习 Public date: 2018-10-25
Title:
A matrix factorization method for predicting miRNA-disease association
Author(s):
LIU Xiaoyan1;  CHEN Xi1;  GUO Maozu1; 2;  CHE Kai1;  WANG Chunyu1
1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China;
2. School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Keywords:
microRNAs; disease; association prediction; matrix factorization; iterative least squares
CLC:
TP391
DOI:
10.11992/tis.201805043
Abstract:
There are increasing evidences that microRNAs (miRNAs) play an important role in life processes. In recent years, predicting the association between miRNAs and diseases has become an active topic. However, most of the existing methods are based on known miRNA-disease associations and are not ideal for miRNAs and diseases without any known associations. This paper presents a least squares optimization matrix factorization method for miRNA-disease association (LMFMDA) prediction. The LMFMDA, which is based on miRNAs similarity matrix, disease similarity matrix, and miRNAs-disease relationship, uses the iterative least squares method to solve the expression vectors of miRNAs and disease and approximates the existing associations between miRNAs and diseases by the expression vector of miRNA and disease. Different from the conventional approach, we introduce auxiliary miRNAs and disease variables to ensure that these variables converge to the optimal solution during optimization. The experiments show that the AUC obtained by applying the leave-one-out cross-validation method is 0.820 6, which is obviously better than other current methods. Especially in the miRNA and disease without any associated information, the LMFMDA algorithm significantly outperforms the latest algorithm.
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