title: Prediction Models for DNA Transcription Termination Based on SOM Networks.
abstract: This paper presents two efficient models for predicting transcription termination (TT) in human DNA. A neural network, self-organizing map, was used for finding features from a human polyadenylation (polyA) sites dataset. We derived prediction models related to different polyA signals. A program, "Dragon PolyAtt", for predicting TT regions was designed for the two most frequent polyA sites "AAUAAA" and "AUUAAA". In our tests, Dragon PolyAtt predicts TT regions with a sensitivity of 48.4% (13.6%) and specificity of 74% (79.1%) when searching for polyA signal "AAUAAA" ("AUUAAA"). Both tests were done on human chromosome 21. Results of Dragon PolyAtt system are substantially better than those obtained by the well-known "polyadq" program.
authors.affiliation: Inst. for Infocomm Res.
authors.forename: V B
authors.initials: VB
authors.lastname: Bajic
authors.forename: T H
authors.initials: TH
authors.lastname: Charn
authors.forename: J X
authors.initials: JX
authors.lastname: Xu
authors.forename: S K
authors.initials: SK
authors.lastname: Panda
authors.forename: S P
authors.initials: SP
authors.lastname: T Krishnan