Prediction Models for DNA Transcription Termination Based on SOM Networks

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

doi:10.1109/IEMBS.2005.1615543

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Journal article

Prediction Models for DNA Transcription Termination Based on SOM Networks

V B Bajic, T H Charn, J X Xu, S K Panda, S P T Krishnan and IEEE

Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), Vol.2005, pp.4791-4794

2005

DOI: https://doi.org/10.1109/IEMBS.2005.1615543

PMID: 17281313

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.

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Title: Prediction Models for DNA Transcription Termination Based on SOM Networks
Creators - without role: V B Bajic - Institute for Infocomm Research
T H Charn
J X Xu
S K Panda
S P T Krishnan
IEEE
Publication Details: Conference proceedings (IEEE Engineering in Medicine and Biology Society. Conf.), Vol.2005, pp.4791-4794
Identifiers: 9943629708331
Academic Unit: King Abdullah University of Science & Technology
Language: English
Resource Type: Journal article