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Invited: Multiclass RNA function classification using next-generation sequencing

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4 Author(s)
Paul Ryvkin ; Penn Center for Bioinformatics, University of Pennsylvania, Philadelphia, USA ; Yuk Yee Leung ; Li-San Wang ; Brian D. Gregory

RNA-seq produces detailed information including length, strand and pairing states, which can be leveraged to characterize RNA functional categories using machine-learning approaches. Using fruit fly small-RNA-seq data, we demonstrate that by combining read length correlation with multi-class classifier models, we can classify four non-coding RNA function classes with high precision.

Published in:

Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on

Date of Conference:

3-5 Feb. 2011