title: OP-Triplet-ELM: Identification of real and pseudo microRNA precursors using extreme learning machine with optimal features.
abstract: MicroRNAs (miRNAs) are a set of short (21-24 nt) non-coding RNAs that play significant regulatory roles in the cells. Triplet-SVM-classifier and MiPred (random forest, RF) can identify the real pre-miRNAs from other hairpin sequences with similar stem-loop (pseudo pre-miRNAs). However, the 32-dimensional local contiguous structure-sequence can induce a great information redundancy. Therefore, it is essential to develop a method to reduce the dimension of feature space. In this paper, we propose optimal features of local contiguous structure-sequences (OP-Triplet). These features can avoid the information redundancy effectively and decrease the dimension of the feature vector from 32 to 8. Meanwhile, a hybrid feature can be formed by combining minimum free energy (MFE) and structural diversity. We also introduce a neural network algorithm called extreme learning machine (ELM). The results show that the specificity ([Formula: see text])and sensitivity ([Formula: see text]) of our method are 92.4% and 91.0%, respectively. Compared with Triplet-SVM-classifier, the total accuracy (ACC) of our ELM method increases by 5%. Compared with MiPred (RF) and miRANN, the total accuracy (ACC) of our ELM method increases nearly by 2%. What is more, our method commendably reduces the dimension of the feature space and the training time.
authors.affiliation: 1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.
authors.forename: Cong
authors.initials: C
authors.lastname: Pian
authors.affiliation: 1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.
authors.forename: Jin
authors.initials: J
authors.lastname: Zhang
authors.affiliation: 1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.
authors.forename: Yuan-Yuan
authors.initials: YY
authors.lastname: Chen
authors.affiliation: 1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Chen
authors.affiliation: 1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.
authors.forename: Qin
authors.initials: Q
authors.lastname: Li
authors.affiliation: 1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Li
authors.affiliation: 1 College of Science, Nanjing Agricultural, University, Nanjing 210095, P. R. China.
authors.forename: Liang-Yun
authors.initials: LY
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D000069550:Machine Learning; D035683:MicroRNAs / Q000235:genetics / Q000378:metabolism; D012322:RNA Precursors
chemical_list: D035683:MicroRNAs; D012322:RNA Precursors
keywords: RNA; algorithm; germ cells; pattern recognition