title: A Meta-Path-Based Prediction Method for Human miRNA-Target Association.
abstract: MicroRNAs (miRNAs) are short noncoding RNAs that play important roles in regulating gene expressing, and the perturbed miRNAs are often associated with development and tumorigenesis as they have effects on their target mRNA. Predicting potential miRNA-target associations from multiple types of genomic data is a considerable problem in the bioinformatics research. However, most of the existing methods did not fully use the experimentally validated miRNA-mRNA interactions. Here, we developed RMLM and RMLMSe to predict the relationship between miRNAs and their targets. RMLM and RMLMSe are global approaches as they can reconstruct the missing associations for all the miRNA-target simultaneously and RMLMSe demonstrates that the integration of sequence information can improve the performance of RMLM. In RMLM, we use RM measure to evaluate different relatedness between miRNA and its target based on different meta-paths; logistic regression and MLE method are employed to estimate the weight of different meta-paths. In RMLMSe, sequence information is utilized to improve the performance of RMLM. Here, we carry on fivefold cross validation and pathway enrichment analysis to prove the performance of our methods. The fivefold experiments show that our methods have higher AUC scores compared with other methods and the integration of sequence information can improve the performance of miRNA-target association prediction.
authors.affiliation: College of Information Science and Electronic Engineering & Collaboration and Innovation Center for Digital Chinese Medicine of 2011 Project of Colleges and Universities in Hunan Province, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Jiawei
authors.identifier: 0000-0003-2385-8272
authors.initials: J
authors.lastname: Luo
authors.affiliation: College of Information Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Cong
authors.initials: C
authors.lastname: Huang
authors.affiliation: College of Information Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.
authors.forename: Pingjian
authors.initials: P
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry* / Q000235:genetics*; D008956:Models, Chemical; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D035683:MicroRNAs