2003 Volume 14 Pages 64-72
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 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.