Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction

Zhenxia Pan; Huaxiang Zhang; Cheng Liang; Guanghui Li; Qiu Xiao; Pingjian Ding; Jiawei Luo

doi:10.1016/j.omtn.2019.06.014

Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction

Mol Ther Nucleic Acids. 2019 Sep 6:17:414-423. doi: 10.1016/j.omtn.2019.06.014. Epub 2019 Jun 28.

Authors

Zhenxia Pan¹, Huaxiang Zhang², Cheng Liang³, Guanghui Li⁴, Qiu Xiao⁵, Pingjian Ding⁶, Jiawei Luo⁶

Affiliations

¹ School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China.
² School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China. Electronic address: huaxzhang@hotmail.com.
³ School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China. Electronic address: alcs417@sdnu.edu.cn.
⁴ School of Information Engineering, East China Jiaotong University, Nanchang 330013, China.
⁵ College of Information Science and Engineering, Hunan Normal University, Changsha 410006, China.
⁶ College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.

Abstract

Researchers have realized that microRNAs (miRNAs) play significant roles in the pathogenesis of various diseases. Although many computational models have been proposed to predict the associations between miRNAs and diseases, prediction performance could still be improved. In this paper, we propose a novel self-weighted, multi-kernel, multi-label learning (SwMKML) method to predict disease-related miRNAs. SwMKML adaptively learns two optimal kernel matrices for both miRNAs and diseases from multiple kernels constructed from known miRNA-disease associations. Moreover, the miRNA-disease associations predicted from both spaces are updated simultaneously based on a multi-label framework. Compared with four state-of-the-art computational models, SwMKML achieved best results of 95.5%, 93.1%, and 84.1% in global leave-one-out cross-validation, 5-fold cross-validation, and overall prediction accuracy, respectively. A case study conducted on head and neck neoplasms further identified two potential prognostic biomarkers, hsa-mir-125b-1 and hsa-mir-125b-2, for the disease. SwMKML is freely available at Github, and we anticipate that it may become an effective tool for potential miRNA-disease association prediction.

Keywords: graph-based learning; miRNA-disease association prediction; multi-kernel learning.