title: Sequence alignment kernel for recognition of promoter regions.
abstract: In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 sigma70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data.
  
  
  BACKGROUND
  The demo version of our method is accessible from our website http://mendel.cs.rhul.ac.uk/
authors.affiliation: Department of Computer Science, Royal Holloway, University of London, Egham, Surrey TW20 0EX, UK. leo@cs.rhul.ac.uk
authors.forename: Leo
authors.initials: L
authors.lastname: Gordon
authors.forename: Alexey Ya
authors.initials: AY
authors.lastname: Chervonenkis
authors.forename: Alex J
authors.initials: AJ
authors.lastname: Gammerman
authors.forename: Ilham A
authors.initials: IA
authors.lastname: Shahmuradov
authors.forename: Victor V
authors.initials: VV
authors.lastname: Solovyev
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D004926:Escherichia coli / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D010363:Pattern Recognition, Automated; D011401:Promoter Regions, Genetic / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*