Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression

Abstract

Abstract:In silico prediction of novel miRNAs from genomic sequences remains a challenging problem. This study presents a genome-wide miRNA discovery software package called GenoScan and evaluates two hairpin classification methods. These methods, one ensemble-based and one using logistic regression were benchmarked along with 15 published methods. In addition, the sequence-folding step is addressed by investigating the impact of secondary structure prediction methods and the choice of input sequence length on prediction performance. Both the accuracy of secondary structure predictions and the miRNA prediction are evaluated. In the benchmark of hairpin classification methods, the regression model achieved highest classification accuracy. Of the structure prediction methods evaluated, ContextFold achieved the highest agreement between predicted and experimentally determined structures. However, both the choice of secondary structure prediction method and input sequence length had limited impact on hairpin classification performance.

计算生物学在rna研究中的应用

“…一些整合性的方法则结合了热力学参数和多序列比对 来估计碱基配对的概率, 如RNAalifold [176] 和TurboFold II [177] . (ⅲ) 传统机器学习方法(classical machine learning): MXfold [178] , Pfold [179] , CONTRAfold [180] 和Context-Fold [181] 等使用传统机器学习算法从已知的RNA结构 数据集中学习并调整模型参数. (ⅳ) 深度学习方法 (deep learning): 利用深度神经网络来捕捉RNA的局部 和全局结构特征进行结构建模.…”