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.
Highlights
MicroRNAs are a small class of endogenous non-coding RNAs with a length of about 20–24 nucleotides [1]
We propose a new computational method of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) based on the assumption that functionally similar miRNAs are often associated with phenotypically similar diseases, and vice versa
We present a novel computational method LMTRDA for predicting miRNA-disease association base on fused multi-source data
Summary
MicroRNAs (miRNAs) are a small class of endogenous non-coding RNAs with a length of about 20–24 nucleotides [1]. Many miRNAs have been discovered and identified by using different biological experimental methods, giving new insights into the functions and regulatory mechanisms of miRNAs [5, 6]. These studies have demonstrated that miRNAs are associated with many important biological processes, such as viral infection [7], immune reaction [8], tumor invasion [9], signal transduction [10], cell proliferation [11], cell growth [12], and cell death [13]. Researchers are committed to finding more efficient computational methods to achieve large-scale and credible predictions of the association among miRNAs and diseases
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