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

• Published in: Journal of biomedical informatics 2018-04, Vol.80, p.26-36
• Main Authors: Ding, Pingjian, Luo, Jiawei, Liang, Cheng, Xiao, Qiu, Cao, Buwen
• Format: Article
• Language: English
• Subjects: Algorithms; Computational Biology - methods; Databases, Genetic; Genetic Association Studies - methods; Graph regularization; Heterogeneousnetwork; Humans; Manifold learning; MicroRNAs - analysis; MicroRNAs - genetics; MicroRNAs - metabolism; miRNA-disease associations; Neoplasms - classification; Neoplasms - genetics; Neoplasms - metabolism; Reproducibility of Results; Target information
• ISSN: 1532-0464; 1532-0480
• DOI: 10.1016/j.jbi.2018.02.013

[Display omitted] •We considers disease gene inference and disease miRNA prediction simultaneously.•Our method does not take any parameters in the prediction process.•Different types of information are combined in the heterogeneous network•We address difficulty in achieving negative sample of disease-related miRNA. 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 conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM.