creators_name: Panwar, Bharat
creators_name: Arora, Amit
creators_name: Raghava, G.P.S.
type: article
datestamp: 2015-07-20 03:41:36
lastmod: 2015-07-20 03:41:36
metadata_visibility: show
title: Prediction and classification of ncRNAs using structural information.
ispublished: pub
subjects: QR
keywords: ncRNA; SVM; RandomForest; Graph properties; Prediction; RNAcon
note: Open Access
abstract: 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.
date: 2014
date_type: published
publication: BMC genomics
volume: 15
publisher: BioMedCentral
pagerange: 127
refereed: TRUE
issn: 1471-2164
official_url: http://www.biomedcentral.com/1471-2164/15/127
citation:   Panwar, Bharat and Arora, Amit and Raghava, G.P.S.  (2014) Prediction and classification of ncRNAs using structural information.  BMC genomics, 15.  p. 127.  ISSN 1471-2164