title: CNCTDiscriminator: coding and noncoding transcript discriminator - an excursion through hypothesis learning and ensemble learning approaches.
abstract: 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 -- 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 -- 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%).
authors.affiliation: Computer Science and Engineering, The University of Texas at Arlington, Arlington, Texas 76019, USA.
authors.forename: Ashis Kumer
authors.initials: AK
authors.lastname: Biswas
authors.forename: Baoju
authors.initials: B
authors.lastname: Zhang
authors.forename: Xiaoyong
authors.initials: X
authors.lastname: Wu
authors.forename: Jean X
authors.initials: JX
authors.lastname: Gao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001482:Base Composition; D019295:Computational Biology; D030561:Databases, Nucleic Acid / Q000706:statistics & numerical data; D059014:High-Throughput Nucleotide Sequencing / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D009690:Nucleic Acid Conformation; D016366:Open Reading Frames; D012333:RNA, Messenger / Q000737:chemistry / Q000235:genetics*; D022661:RNA, Untranslated / Q000737:chemistry / Q000235:genetics*; D017423:Sequence Analysis, RNA / Q000706:statistics & numerical data*; D012984:Software
chemical_list: D012333:RNA, Messenger; D022661:RNA, Untranslated