@inproceedings{d56a1035f8494ebeb02377b9bcec5b42,
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.",
keywords = "Bioinfomatics, Polyadenylation sites, Self-organizing maps, Transcription termination",
author = "Bajic, {V. B.} and Charn, {T. H.} and Xu, {J. X.} and Panda, {S. K.} and Krishnan, {S. P.T.}",
year = "2005",
doi = "10.1109/iembs.2005.1615543",
language = "English (US)",
isbn = "0780387406",
series = "Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4791--4794",
booktitle = "Proceedings of the 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005",
address = "United States",
note = "2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005 ; Conference date: 01-09-2005 Through 04-09-2005",
}