title: Hybrid MM/SVM structural sensors for stochastic sequential data.
abstract: In this paper we present preliminary results stemming from a novel application of Markov Models and Support Vector Machines to splice site classification of Intron-Exon and Exon-Intron (5' and 3') splice sites. We present the use of Markov based statistical methods, in a log likelihood discriminator framework, to create a non-summed, fixed-length, feature vector for SVM-based classification. We also explore the use of Shannon-entropy based analysis for automated identification of minimal-size models (where smaller models have known information loss according to the specified Shannon entropy representation). We evaluate a variety of kernels and kernel parameters in the classification effort. We present results of the algorithms for splice-site datasets consisting of sequences from a variety of species for comparison.
authors.affiliation: Department of Computer Science, University of New Orleans, LA 70148, USA. broux@cs.uno.edu
authors.forename: Brian
authors.initials: B
authors.lastname: Roux
authors.forename: Stephen
authors.initials: S
authors.lastname: Winters-Hilt
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003627:Data Interpretation, Statistical; D008390:Markov Chains; D010363:Pattern Recognition, Automated / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D013269:Stochastic Processes
references.citation: BMC Bioinformatics. 2006 Sep 06;7 Suppl 2:S4
references.pmid: 17118147