In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm

Jia Qu; Xing Chen; Ya-Zhou Sun; Yan Zhao; Shu-Bin Cai; Zhong Ming; Zhu-Hong You; Jian-Qiang Li

doi:10.1016/j.omtn.2018.12.002

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm

Mol Ther Nucleic Acids. 2019 Mar 1:14:274-286. doi: 10.1016/j.omtn.2018.12.002. Epub 2018 Dec 13.

Authors

Jia Qu¹, Xing Chen², Ya-Zhou Sun³, Yan Zhao¹, Shu-Bin Cai³, Zhong Ming⁴, Zhu-Hong You⁵, Jian-Qiang Li³

Affiliations

¹ School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China.
² School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China. Electronic address: xingchen@amss.ac.cn.
³ College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China.
⁴ College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China. Electronic address: mingz@szu.edu.cn.
⁵ Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Science, Ürümqi 830011, China. Electronic address: zhuhongyou@ms.xjb.ac.cn.

Abstract

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.

Keywords: HeteSim algorithm; association prediction; heterogeneous network; microRNA; small molecule.