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In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm
Qu, J (Qu, Jia)[ 1 ]; Chen, X (Chen, Xing)[ 1 ]; Sun, YZ (Sun, Ya-Zhou)[ 2 ]; Zhao, Y (Zhao, Yan)[ 1 ]; Cai, SB (Cai, Shu-Bin)[ 2 ]; Ming, Z (Ming, Zhong)[ 2 ]; You, ZH (You, Zhu-Hong)[ 3 ]; Li, JQ (Li, Jian-Qiang)[ 2 ]
2019
Source PublicationMOLECULAR THERAPY-NUCLEIC ACIDS
ISSN2162-2531
Volume14Issue:3Pages:274-286
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.

DOI10.1016/j.omtn.2018.12.002
Indexed BySCI
WOS IDWOS:000460323000022
Citation statistics
Document Type期刊论文
Identifierhttp://ir.xjipc.cas.cn/handle/365002/5721
Collection多语种信息技术研究室
Corresponding AuthorYou, ZH (You, Zhu-Hong)[ 3 ]
Affiliation1.China Univ Min & Technol, Sch Informat & Control Engn, 1 Daxue Rd, Xuzhou 221116, Jiangsu, Peoples R China
2.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China
3.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China
Recommended Citation
GB/T 7714
Qu, J ,Chen, X ,Sun, YZ ,et al. In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm[J]. MOLECULAR THERAPY-NUCLEIC ACIDS,2019,14(3):274-286.
APA Qu, J .,Chen, X .,Sun, YZ .,Zhao, Y .,Cai, SB .,...&Li, JQ .(2019).In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm.MOLECULAR THERAPY-NUCLEIC ACIDS,14(3),274-286.
MLA Qu, J ,et al."In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm".MOLECULAR THERAPY-NUCLEIC ACIDS 14.3(2019):274-286.
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