OSCAR: One-class SVM for accurate recognition ofcis-elements | Zendy

Bo Jiang | Zendy; Michael Q. Zhang | Zendy; Xuegong Zhang | Zendy

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OSCAR: One-class SVM for accurate recognition ofcis-elements

Author(s) -

Bo Jiang,

Michael Q. Zhang,

Xuegong Zhang

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm473

Subject(s) - support vector machine , computer science , transcription factor , artificial intelligence , motif (music) , class (philosophy) , machine learning , data mining , pattern recognition (psychology) , computational biology , biology , genetics , gene , physics , acoustics

Traditional methods to identify potential binding sites of known transcription factors still suffer from large number of false predictions. They mostly use sequence information in a position-specific manner and neglect other types of information hidden in the proximal promoter regions. Recent biological and computational researches, however, suggest that there exist not only locational preferences of binding, but also correlations between transcription factors.

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