TY  - JOUR
N1  - Open Access
ID  - open1711
UR  - http://www.biomedcentral.com/1471-2164/15/127
A1  - Panwar, Bharat
A1  - Arora, Amit
A1  - Raghava, G.P.S.
Y1  - 2014///
N2  - Evidence is accumulating that non-coding transcripts, previously thought to be functionally inert, play important roles in various cellular activities. High throughput techniques like next generation sequencing have resulted in the generation of vast amounts of sequence data. It is therefore desirable, not only to discriminate coding and non-coding transcripts, but also to assign the noncoding RNA (ncRNA) transcripts into respective classes (families). Although there are several algorithms available for this task, their classification performance remains a major concern. Acknowledging the crucial role that non-coding transcripts play in cellular processes, it is required to develop algorithms that are able to precisely classify ncRNA transcripts.
PB  - BioMedCentral
JF  - BMC genomics
VL  - 15
KW  - ncRNA; SVM; RandomForest; Graph properties; Prediction; RNAcon
SN  - 1471-2164
TI  - Prediction and classification of ncRNAs using structural information.
ER  -