SusMiRPred: ab initio SVM classification for porcine microRNA precursor prediction
MicroRNA (miRNA), which is short non-coding RNA, plays important roles in almost all biological processes examined. Several classifiers have been applied to predict humans, mice and rats precursor miRNAs (pre-miRNAs), but no classifier is applied to classify porcine pre-miRNAs only based on the porcine pre-miRNAs because of little known miRNA component in the porcine genome. Here, we developed a novel classifier, called SusMiRPred, to predicted porcine pre-miRNAs. Trained on 60 porcine pre-miRNAs and 65 pseudo procine hairpins, SusMiRPred achieve 86.4% (5-fold cross-validation accuracy) and 0.9144 (ROC score). Tested on the remaining 14 porcine premiRNAs and 1000 pseudo hairpins, it reports 100% (sensitivity), 87.3% (specificity) and 87.5% (accuracy). SusMiRPred was proved an effective ab initio Support Vector Machine (SVM) classifier for predicting porcine pre-miRNAs and encapsulated with a Java package for other users utilizing it expedient. Furthermore, another Java package, called SusMiRFilter, was developed to filter out the short sequences which have not the pre-miRNAs sequence structure features.
Peng-Fang Zhou Fei Zhang Yang Zhang Zhen-Hua Zhao Wen-Qian Zhang De-Li Zhang
Investigation Group of Molecular Virology, Immunology,Oncology & Systems Biology, Center for Bioinfo Investigation Group of Molecular Virology, Immunology, Oncology & Systems Biology, Center for Bioinf
国际会议
成都
英文
1-3
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）