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|Title:||Prediction models for DNA transcription termination based on SOM networks|
|Citation:||Bajic, V.B.,Charn, T.H.,Xu, J.X.,Panda, S.K.,Krishnan, S.P.T. (2005). Prediction models for DNA transcription termination based on SOM networks. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings 7 VOLS : 4791-4794. ScholarBank@NUS Repository.|
|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. © 2005 IEEE.|
|Source Title:||Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings|
|Appears in Collections:||Staff Publications|
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