Bajic VB, Charn TH, Xu JX, Panda SK, T Krishnan SP. Prediction Models for DNA Transcription Termination Based on SOM Networks.
Conf Proc IEEE Eng Med Biol Soc 2012;
2005:4791-4. [PMID:
17281313 DOI:
10.1109/iembs.2005.1615543]
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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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