{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T18:30:33Z","timestamp":1778869833808,"version":"3.51.4"},"reference-count":19,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Bioinform. Comput. Biol."],"published-print":{"date-parts":[[2013,10]]},"abstract":"<jats:p> The statistics about the open reading frames, the base compositions and the properties of the predicted secondary structures have potential to address the problem of discriminating coding and noncoding transcripts. Again, the Next Generation Sequencing platform, RNA-seq, provides us bounty of data from which expression profiles of the transcripts can be extracted which urged us adding a new set of dimension in this classification task. In this paper, we proposed CNCTDiscriminator \u2014 a coding and noncoding transcript discriminating system where we applied the integration of these four categories of features about the transcripts. The feature integration was done using both hypothesis learning and feature specific ensemble learning approaches. The CNCTDiscriminator model which was trained with composition and ORF features outperforms (precision 83.86%, recall 82.01%) other three popular methods \u2014 CPC (precision 98.31%, recall 25.95%), CPAT (precision 97.74%, recall 52.50%) and PORTRAIT (precision 84.37%, recall 73.2%) when applied to an independent benchmark dataset. However, the CNCTDiscriminator model that was trained using the ensemble approach shows comparable performance (precision 89.85%, recall 71.08%). <\/jats:p>","DOI":"10.1142\/s021972001342002x","type":"journal-article","created":{"date-parts":[[2013,8,5]],"date-time":"2013-08-05T21:55:57Z","timestamp":1375739757000},"page":"1342002","source":"Crossref","is-referenced-by-count":6,"title":["CNCTDISCRIMINATOR: CODING AND NONCODING TRANSCRIPT DISCRIMINATOR \u2014 AN EXCURSION THROUGH HYPOTHESIS LEARNING AND ENSEMBLE LEARNING APPROACHES"],"prefix":"10.1142","volume":"11","author":[{"given":"ASHIS KUMER","family":"BISWAS","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, The University of Texas at Arlington, Arlington, Texas 76019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"BAOJU","family":"ZHANG","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Information, Tianjin Normal University, Tianjin 300387, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"XIAOYONG","family":"WU","sequence":"additional","affiliation":[{"name":"School of Physics and Electronic Information, Tianjin Normal University, Tianjin 300387, P. R. 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