Prediction Models for DNA Transcription Termination Based on SOM Networks

Author

Bajic, V.B. ; Charn, T.H. ; Xu, J.X. ; Panda, S.K. ; Krishnan, S.P.T.

Author_Institution

Inst. for Infocomm Res.

fYear

2006

Firstpage

4791

Lastpage

4794

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

Keywords

DNA; biology computing; cellular biophysics; molecular biophysics; physiological models; self-organising feature maps; DNA transcription termination; Dragon PolyAtt; SOM networks; TT regions; human chromosome 21; human polyadenylation sites dataset; neural network; prediction models; self-organizing map; Bioinformatics; Biological cells; DNA; Digital multimedia broadcasting; Genomics; Humans; Neural networks; Predictive models; Signal processing; Testing; Bioinfomatics; Polyadenylation sites; Self-Organizing Maps; Transcription Termination;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

Conference_Location

Shanghai

Print_ISBN

0-7803-8741-4

Type

conf

DOI

10.1109/IEMBS.2005.1615543

Filename

1615543