Human disease MiRNA inference by combining target information based on heterogeneous manifolds

Journal of Biomedical Informatics
Pingjian DingBuwen Cao

Abstract

The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omics data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conduc...Continue Reading

Citations

Oct 1, 2019·Briefings in Bioinformatics·Pingjian DingChee-Keong Kwoh
Jul 14, 2019·Methods : a Companion to Methods in Enzymology·Donglin GuoMin Li
Jan 14, 2021·Biotechnology and Bioprocess Engineering : BBE·Hyunho KimHojung Nam

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