Title :
Invited: Multiclass RNA function classification using next-generation sequencing
Author :
Ryvkin, Paul ; Leung, Yuk Yee ; Wang, Li-San ; Gregory, Brian D.
Author_Institution :
Penn Center for Bioinf., Univ. of Pennsylvania, Philadelphia, PA, USA
Abstract :
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.
Keywords :
biology computing; molecular biophysics; molecular configurations; organic compounds; machine-learning approaches; multiclass RNA function classification; multiclass classifier models; next-generation sequencing; noncoding RNA function class; small-RNA-seq data; Accuracy; Bioinformatics; Feature extraction; Genomics; Next generation networking; RNA; Support vector machines; multi-class classification; next-generation RNA sequencing;
Conference_Titel :
Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-61284-851-8
DOI :
10.1109/ICCABS.2011.5729859
