Abstract:
:MiRNAs play important roles in many diseases including cancers. However computational prediction of miRNA target genes is challenging and the accuracies of existing methods remain poor. We report mirMark, a new machine learning-based method of miRNA target prediction at the site and UTR levels. This method uses experimentally verified miRNA targets from miRecords and mirTarBase as training sets and considers over 700 features. By combining Correlation-based Feature Selection with a variety of statistical or machine learning methods for the site- and UTR-level classifiers, mirMark significantly improves the overall predictive performance compared to existing publicly available methods. MirMark is available from https://github.com/lanagarmire/MirMark.
journal_name
Genome Bioljournal_title
Genome biologyauthors
Menor M,Ching T,Zhu X,Garmire D,Garmire LXdoi
10.1186/s13059-014-0500-5subject
Has Abstractpub_date
2014-01-01 00:00:00pages
500issue
10eissn
1474-7596issn
1474-760Xpii
s13059-014-0500-5journal_volume
15pub_type
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