title: Splice site detection with a higher-order markov model implemented on a neural network.
abstract: The performance of the ab inito gene prediction approaches mostly depends on the effectiveness of detecting the splice sites. This paper addresses the problem of splice site detection using higher-order Markov models. The tenet of our approach is to brace the higher-order dependencies of a Markov model by a neural network that receives the inputs from low-order Markov chains. The method is able not only to capture the higher-order dependencies in the bases of the consensus sequence immediately surrounding the splice site but also to distinguish the characteristics of the coding and non-coding regions on both sides of the splice site. Our experiments indicate that the present method achieves better accuracies over the techniques employing low-order Markov chains and other earlier approaches.
authors.affiliation: School of Computer Engineering Nanyang Technological University, Singapore 639798. hsl@pmail.ntu.edu.sg
authors.forename: Loi Sy
authors.initials: LS
authors.lastname: Ho
authors.forename: Jagath C
authors.initials: JC
authors.lastname: Rajapakse
mesh_terms: D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D005091:Exons / Q000235:genetics; D015894:Genome, Human; D006801:Humans; D007438:Introns / Q000235:genetics; D008390:Markov Chains; D008969:Molecular Sequence Data; D009415:Nerve Net