TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples | Zendy

Sanghamitra Bandyopadhyay | Zendy; Ramkrishna Mitra | Zendy

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TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples

Author(s) -

Sanghamitra Bandyopadhyay,

Ramkrishna Mitra

Publication year - 2009

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btp503

Subject(s) - support vector machine , classifier (uml) , computer science , artificial intelligence , machine learning , identification (biology) , test set , set (abstract data type) , feature vector , data mining , pattern recognition (psychology) , biology , botany , programming language

Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training.

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