title: Genome-wide discovery of miRNAs using ensembles of machine learning algorithms and logistic regression.
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.
authors.forename: Benjamin
authors.initials: B
authors.lastname: Ulfenborg
authors.forename: Karin
authors.initials: K
authors.lastname: Klinga-Levan
authors.forename: Björn
authors.initials: B
authors.lastname: Olsson
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D015894:Genome, Human / Q000235:genetics*; D006801:Humans; D055029:Inverted Repeat Sequences / Q000235:genetics; D016015:Logistic Models; D000069550:Machine Learning; D035683:MicroRNAs / Q000235:genetics*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis; D017423:Sequence Analysis, RNA / Q000379:methods*
chemical_list: D035683:MicroRNAs