title: StackTIS: a stacked generalization approach for effective prediction of translation initiation sites.
abstract: The prediction of the translation initiation site in an mRNA or cDNA sequence is an essential step in gene prediction and an open research problem in bioinformatics. Although recent approaches perform well, more effective and reliable methodologies are solicited. We developed an adaptable data mining method, called StackTIS, which is modular and consists of three prediction components that are combined into a meta-classification system, using stacked generalization, in a highly effective framework. We performed extensive experiments on sequences of two diverse eukaryotic organisms (Homo sapiens and Oryza sativa), indicating that StackTIS achieves statistically significant improvement in performance.
authors.affiliation: Department of Informatics, Aristotle University of Thessaloniki, Greece. gtzanis@csd.auth.gr
authors.forename: George
authors.initials: G
authors.lastname: Tzanis
authors.forename: Christos
authors.initials: C
authors.lastname: Berberidis
authors.forename: Ioannis
authors.initials: I
authors.lastname: Vlahavas
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D018076:DNA, Complementary / Q000235:genetics; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D006801:Humans; D012275:Oryza / Q000235:genetics; D014176:Protein Biosynthesis; D012333:RNA, Messenger / Q000235:genetics; D017422:Sequence Analysis, DNA; D017423:Sequence Analysis, RNA; D060388:Support Vector Machine; D024363:Transcription Initiation Site
chemical_list: D018076:DNA, Complementary; D012333:RNA, Messenger