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PLoS Comput Biol. 2019 Mar 27;15(3):e1006865. doi: 10.1371/journal.pcbi.1006865. eCollection 2019 Mar.

LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities.

Author information

1
Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Urumqi, China.
2
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
3
Department of Electrical Computer and Telecommunications Engineering Technology, Rochester Institute of Technology, Rochester, United States of America.
4
Xiangya School of Public Health, Central South University, Changsha, China.

Abstract

Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases.

PMID:
30917115
DOI:
10.1371/journal.pcbi.1006865
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Conflict of interest statement

The authors have declared that no competing interests exist.

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