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Genomics. 2019 May 25. pii: S0888-7543(19)30191-0. doi: 10.1016/j.ygeno.2019.05.021. [Epub ahead of print]

Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization.

Author information

1
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China. Electronic address: xingchen@amss.ac.cn.
2
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.

Abstract

Many biological experimental studies have confirmed that microRNAs (miRNAs) play a significant role in human complex diseases. Exploring miRNA-disease associations could be conducive to understanding disease pathogenesis at the molecular level and developing disease diagnostic biomarkers. However, since conducting traditional experiments is a costly and time-consuming way, plenty of computational models have been proposed to predict miRNA-disease associations. In this study, we presented a neoteric Bayesian model (KBMFMDA) that combines kernel-based nonlinear dimensionality reduction, matrix factorization and binary classification. The main idea of KBMFMDA is to project miRNAs and diseases into a unified subspace and estimate the association network in that subspace. KBMFMDA obtained the AUCs of 0.9132, 0.8708, 0.9008±0.0044 in global and local leave-one-out and five-fold cross validation. Moreover, KBMFMDA was applied to three important human cancers in three different kinds of case studies and most of the top 50 potential disease-related miRNAs were confirmed by many experimental reports.

KEYWORDS:

Association prediction; Bayesian algorithm; Conjugate probabilistic model; Disease; Matrix factorization; microRNA

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