Splice site detection with a higher-order markov model implemented on a neural network

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.