miRNAfe: A comprehensive tool for feature extraction in microRNA prediction

Bonnet, 2004, Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences, Bioinformatics, 20, 2911, 10.1093/bioinformatics/bth374 Goro, 2007, miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity, RNA, 13, 2081, 10.1261/rna.655107 Gudyś, 2013, HuntMi: an efficient and taxon-specific approach in pre-miRNA identification, BMC bioinform., 14, 83, 10.1186/1471-2105-14-83 Hackenberg, 2009, miRanalyzer: a microRNA detection and analysis tool for next-generation sequencing experiments, Nucleic Acids Res., 37, 68, 10.1093/nar/gkp347 Hertel, 2006, Hairpins in a Haystack: recognizing microRNA precursors in comparative genomics data, Bioinformatics, 22, e197, 10.1093/bioinformatics/btl257 Huang, 2007, MiRF inder: an improved approach and software implementation for genome-wide fast microRNA precursor scans, BMC Bioinform., 8, 341, 10.1186/1471-2105-8-341 Jiandong, 2010, MiRenSVM: towards better prediction of microRNA precursors using an ensemble SVM classifier with multi-loop features, BMC Bioinform., 11, 11 Jiang, 2007, MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features, Nucleic Acids Res., 35, W339, 10.1093/nar/gkm368 Kleftogiannis, 2013, Where we stand, where we are moving: surveying computational techniques for identifying miRNA genes and uncovering their regulatory role, J. Biomed. Inform., 46, 563, 10.1016/j.jbi.2013.02.002 Kozomara, 2014, miRBase: annotating high confidence microRNAs using deep sequencing data, Nucleic Acids Res., 42, D68, 10.1093/nar/gkt1181 Lamers, 2014, HIV-associated neuropathogenesis: a systems biology perspective for modeling and therapy, Biosystems, 119, 53, 10.1016/j.biosystems.2014.04.002 Li, 2010, Computational approaches for microRNA studies: a review, Mamm Genome, 21, 1, 10.1007/s00335-009-9241-2 Lim, 2003, The microRNAs of Caenorhabditis elegans, Genes Dev., 17, 991, 10.1101/gad.1074403 Lopes, 2014, The discriminant power of RNA features for pre-miRNA recognition, BMC Bioinform., 15, 124, 10.1186/1471-2105-15-124 McCaskill, 1990, The equilibrium partition function and base pair binding probabilities for RNA secondary structure, Biopolymers, 29, 1105, 10.1002/bip.360290621 Ng, 2007, De novo SVM classification of precursor microRNAs from genomic pseudo hairpins using global and intrinsic folding measures, Bioinformatics, 23, 1321, 10.1093/bioinformatics/btm026 Rukshan, 2009, microPred: effective classification of pre-miRNAs for human miRNA gene prediction, Bioinformatics, 25, 989, 10.1093/bioinformatics/btp107 Stegmayer, 2015, A very simple and fast way to access and validate algorithms in reproducible research, Brief. Bioinform., 10.1093/bib/bbv054 Terai, 2007, miRRim: a novel system to find conserved miRNAs with high sensitivity and specificity, RNA, 13, 2081, 10.1261/rna.655107 Xuan, 2011, Genetic algorithm-based efficient feature selection for classification of pre-miRNAs, Genet. Mol. Res., 10, 588, 10.4238/vol10-2gmr969 Xue, 2005, Classification of real and pseudo microRNA precursors using local structure-sequence features and support vector machine, BMC Bioinform., 6, 310, 10.1186/1471-2105-6-310 Yousef, 2006, Combining multi-species genomic data for microRNA identification using a Naive Bayes classifier, Bioinformatics, 22, 1325, 10.1093/bioinformatics/btl094 Zhang, 2010, Characteristic comparison between two types of miRNA precursors in metazoan species, Biosystems, 100, 144, 10.1016/j.biosystems.2010.02.009 Zuker, 1981, Optimal computer folding of large RNA sequences using thermodynamic and auxiliary information, Nucl. Acid, 9, 133, 10.1093/nar/9.1.133