Predicting MiRNA-Disease Association by Latent Feature Extraction with Positive Samples

被引:19
作者
Che, Kai [1 ]
Guo, Maozu [1 ,2 ,3 ]
Wang, Chunyu [1 ]
Liu, Xiaoyan [1 ]
Chen, Xi [1 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci & Technol, Harbin 150001, Heilongjiang, Peoples R China
[2] Beijing Univ Civil Engn & Architecture, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
[3] Beijing Key Lab Intelligent Proc Bldg Big Data, Beijing 100044, Peoples R China
基金
中国国家自然科学基金;
关键词
microRNAs; disease; association prediction; latent feature extraction; MICRORNAS; DATABASE;
D O I
10.3390/genes10020080
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
In discovering disease etiology and pathogenesis, the associations between MicroRNAs (miRNAs) and diseases play a critical role. Given known miRNA-disease associations (MDAs), how to uncover potential MDAs is an important problem. To solve this problem, most of the existing methods regard known MDAs as positive samples and unknown ones as negative samples, and then predict possible MDAs by iteratively revising the negative samples. However, simply viewing unknown MDAs as negative samples introduces erroneous information, which may result in poor predication performance. To avoid such defects, we present a novel method using only positive samples to predict MDAs by latent features extraction (LFEMDA). We design a new approach to construct the miRNAs similarity matrix. LFEMDA integrates the disease similarity matrix, the known MDAs and the miRNAs similarity matrix to identify potential MDAs. By introducing miRNAs and diseases knowledge as the auxiliary variables, the method can converge to give the optimal solution in each iteration. We conduct experiments on high-association diseases and new diseases datasets, in which our method shows better performance than that of other methods. We also carry out a case study on breast neoplasms to further demonstrate the capacity of our method in uncovering potential MDAs.
引用
收藏
页数:14
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