title: A novel two-layer SVM model in miRNA Drosha processing site detection.
abstract: BACKGROUND
  MicroRNAs (miRNAs) are a large class of non-coding RNAs with important functions wide spread in animals, plants and viruses. Studies showed that an RNase III family member called Drosha recognizes most miRNAs, initiates their processing and determines the mature miRNAs. The Drosha processing sites identification will shed some light on both miRNA identification and understanding the mechanism of Drosha processing.
  
  
  METHODS
  We developed a computational method for Drosha processing site predicting, named as DroshaPSP, which employs a two-layer mathematical model to integrate structure feature in the first layer and sequence features in the second layer. The performance of DroshaPSP was estimated by 5-fold cross-validation and measured by ACC (accuracy), Sn (sensitivity), Sp (specificity), P (precision) and MCC (Matthews correlation coefficient).
  
  
  RESULTS
  The results of testing DroshaPSP on the miRNA data of Drosophila melanogaster indicated that the Sn, Sp, and MCC thereof reach to 0.86, 0.99 and 0.86 respectively.
  
  
  CONCLUSIONS
  We found the Shannon entropy, a chemical kinetics feature, is a significant feature in telling the true sites among the nearby sites and improving the performance.
authors.forename: Xingchi
authors.initials: X
authors.lastname: Hu
authors.forename: Chuang
authors.initials: C
authors.lastname: Ma
authors.forename: Yanhong
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000818:Animals; D019295:Computational Biology / Q000379:methods*; D004331:Drosophila melanogaster / Q000378:metabolism; D035683:MicroRNAs / Q000378:metabolism*; D015233:Models, Statistical; D012323:RNA Processing, Post-Transcriptional; D043244:Ribonuclease III / Q000378:metabolism*; D060388:Support Vector Machine
chemical_list: D035683:MicroRNAs; D043244:Ribonuclease III