MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources

Journal of Translational Medicine
Kai ZhengZheng-Wei Li

Abstract

Emerging evidences show that microRNA (miRNA) plays an important role in many human complex diseases. However, considering the inherent time-consuming and expensive of traditional in vitro experiments, more and more attention has been paid to the development of efficient and feasible computational methods to predict the potential associations between miRNA and disease. In this work, we present a machine learning-based model called MLMDA for predicting the association of miRNAs and diseases. More specifically, we first use the k-mer sparse matrix to extract miRNA sequence information, and combine it with miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity information. Then, more representative features are extracted from them through deep auto-encoder neural network (AE). Finally, the random forest classifier is used to effectively predict potential miRNA-disease associations. The experimental results show that the MLMDA model achieves promising performance under fivefold cross validations with AUC values of 0.9172, which is higher than the methods using different classifiers or different feature combination methods mentioned in this paper. In addition, to further evaluate ...Continue Reading

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Citations

May 19, 2020·PLoS Computational Biology·Kai ZhengYu-An Huang
Jun 26, 2020·Molecular Genetics and Genomics : MGG·K Deepthi, A S Jereesh
Jun 6, 2020·Molecular Genetics and Genomics : MGG·Jiashu LiWenzhang Bao
Sep 10, 2020·Computational Biology and Chemistry·Dariusz Mrozek
Apr 27, 2021·Frontiers in Genetics·Mingyan TangLei Deng
Jun 30, 2021·Interdisciplinary Sciences, Computational Life Sciences·Guobo XieJianming Li
Jul 24, 2021·Briefings in Bioinformatics·Zhengwei LiWenzheng Bao

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Software Mentioned

MLMDA
NNMDA
sim
LMTRDA

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