title: Prediction of plant pre-microRNAs and their microRNAs in genome-scale sequences using structure-sequence features and support vector machine.
abstract: BACKGROUND
  MicroRNAs (miRNAs) are a family of non-coding RNAs approximately 21 nucleotides in length that play pivotal roles at the post-transcriptional level in animals, plants and viruses. These molecules silence their target genes by degrading transcription or suppressing translation. Studies have shown that miRNAs are involved in biological responses to a variety of biotic and abiotic stresses. Identification of these molecules and their targets can aid the understanding of regulatory processes. Recently, prediction methods based on machine learning have been widely used for miRNA prediction. However, most of these methods were designed for mammalian miRNA prediction, and few are available for predicting miRNAs in the pre-miRNAs of specific plant species. Although the complete Solanum lycopersicum genome has been published, only 77 Solanum lycopersicum miRNAs have been identified, far less than the estimated number. Therefore, it is essential to develop a prediction method based on machine learning to identify new plant miRNAs.
  
  
  RESULTS
  A novel classification model based on a support vector machine (SVM) was trained to identify real and pseudo plant pre-miRNAs together with their miRNAs. An initial set of 152 novel features related to sequential structures was used to train the model. By applying feature selection, we obtained the best subset of 47 features for use with the Back Support Vector Machine-Recursive Feature Elimination (B-SVM-RFE) method for the classification of plant pre-miRNAs. Using this method, 63 features were obtained for plant miRNA classification. We then developed an integrated classification model, miPlantPreMat, which comprises MiPlantPre and MiPlantMat, to identify plant pre-miRNAs and their miRNAs. This model achieved approximately 90% accuracy using plant datasets from nine plant species, including Arabidopsis thaliana, Glycine max, Oryza sativa, Physcomitrella patens, Medicago truncatula, Sorghum bicolor, Arabidopsis lyrata, Zea mays and Solanum lycopersicum. Using miPlantPreMat, 522 Solanum lycopersicum miRNAs were identified in the Solanum lycopersicum genome sequence.
  
  
  CONCLUSIONS
  We developed an integrated classification model, miPlantPreMat, based on structure-sequence features and SVM. MiPlantPreMat was used to identify both plant pre-miRNAs and the corresponding mature miRNAs. An improved feature selection method was proposed, resulting in high classification accuracy, sensitivity and specificity.
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. mengjun@dlut.edu.cn.
authors.forename: Jun
authors.initials: J
authors.lastname: Meng
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. kobe_liudong@mail.dlut.edu.cn.
authors.forename: Dong
authors.initials: D
authors.lastname: Liu
authors.affiliation: School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, 116023, China. 253376871@qq.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Sun
authors.affiliation: School of Life Science and Biotechnology, Dalian University of Technology, Dalian, Liaoning, 116023, China. luanyush@dlut.edu.cn.
authors.forename: Yushi
authors.initials: Y
authors.lastname: Luan
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D001185:Artificial Intelligence; D001483:Base Sequence; D019295:Computational Biology / Q000379:methods*; D018745:Genome, Plant; D018551:Solanum lycopersicum / Q000235:genetics; D035683:MicroRNAs / Q000737:chemistry / Q000235:genetics*; D008969:Molecular Sequence Data; D010944:Plants / Q000235:genetics*; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs