title: De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures.
abstract: BACKGROUND
  MicroRNAs (miRNAs) are small ncRNAs participating in diverse cellular and physiological processes through the post-transcriptional gene regulatory pathway. Critically associated with the miRNAs biogenesis, the hairpin structure is a necessary feature for the computational classification of novel precursor miRNAs (pre-miRs). Though many of the abundant genomic inverted repeats (pseudo hairpins) can be filtered computationally, novel species-specific pre-miRs are likely to remain elusive.
  
  
  RESULTS
  miPred is a de novo Support Vector Machine (SVM) classifier for identifying pre-miRs without relying on phylogenetic conservation. To achieve significantly higher sensitivity and specificity than existing (quasi) de novo predictors, it employs a Gaussian Radial Basis Function kernel (RBF) as a similarity measure for 29 global and intrinsic hairpin folding attributes. They characterize a pre-miR at the dinucleotide sequence, hairpin folding, non-linear statistical thermodynamics and topological levels. Trained on 200 human pre-miRs and 400 pseudo hairpins, miPred achieves 93.50% (5-fold cross-validation accuracy) and 0.9833 (ROC score). Tested on the remaining 123 human pre-miRs and 246 pseudo hairpins, it reports 84.55% (sensitivity), 97.97% (specificity) and 93.50% (accuracy). Validated onto 1918 pre-miRs across 40 non-human species and 3836 pseudo hairpins, it yields 87.65% (92.08%), 97.75% (97.42%) and 94.38% (95.64%) for the mean (overall) sensitivity, specificity and accuracy. Notably, A.mellifera, A.geoffroyi, C.familiaris, E.Barr, H. Simplex virus, H.cytomegalovirus, O.aries, P.patens, R.lymphocryptovirus, Simian virus and Z.mays are unambiguously classified with 100.00% (sensitivity) and >93.75% (specificity).
  
  
  BACKGROUND
  Data sets, raw statistical results and source codes are available at http://web.bii.a-star.edu.sg/~stanley/Publications
authors.affiliation: Bioinformatics Institute, Singapore. stanley@bii.a-star.edu.sg
authors.forename: Kwang Loong Stanley
authors.initials: KL
authors.lastname: Ng
authors.forename: Santosh K
authors.initials: SK
authors.lastname: Mishra
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D035683:MicroRNAs / Q000737:chemistry* / Q000235:genetics / Q000648:ultrastructure*; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009690:Nucleic Acid Conformation; D010363:Pattern Recognition, Automated / Q000379:methods*; D012322:RNA Precursors / Q000737:chemistry* / Q000235:genetics / Q000648:ultrastructure*; D016415:Sequence Alignment / Q000379:methods*; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D035683:MicroRNAs; D012322:RNA Precursors