@article{open1711,
          volume = {15},
          author = {Bharat Panwar and Amit Arora and G.P.S. Raghava},
            note = {Open Access},
           title = {Prediction and classification of ncRNAs using structural information.},
       publisher = {BioMedCentral},
         journal = {BMC genomics},
           pages = {127},
            year = {2014},
        keywords = {ncRNA; SVM; RandomForest; Graph properties; Prediction; RNAcon},
             url = {http://crdd.osdd.net/open/1711/},
        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.}
}