Modeling transcription termination of selected gene groups using support vector machine

J. X. Xu*, B. Ashok, S. K. Panda, Vladimir Bajic

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this work we use support vector machine to predict polyadenylation sites (Poly (A) sites) in human DNA and mRNA sequences by analyzing features around them. Two models are created. The first model identifies the possible location of the Poly (A) site effectively. The second model distinguishes between true and false Poly (A) sites, hence effectively detect the region where Poly (A) sites and transcription termination occurs. The support vector machine (SVM) approach achieves almost 90% sensitivity, 83% accuracy, 80% precision and 76% specificity on tests of the chromosomal data such as chromosome 21, The models are able to make on average just about one false prediction every 7000 nucleotides. In most cases, better results can be achieved in comparison with those reported previously on the same data sets.

Original languageEnglish (US)
Title of host publication2008 International Joint Conference on Neural Networks, IJCNN 2008
Pages384-389
Number of pages6
DOIs
StatePublished - Nov 24 2008
Event2008 International Joint Conference on Neural Networks, IJCNN 2008 - Hong Kong, China
Duration: Jun 1 2008Jun 8 2008

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2008 International Joint Conference on Neural Networks, IJCNN 2008
CountryChina
CityHong Kong
Period06/1/0806/8/08

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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