Splice site detection using pruned maximum likelihood model

In this paper we propose a novel method for splice site prediction using the maximum likelihood model. We performed maximum likelihood over the acceptor and donor datasets, and calculated sensitivity to measure the prediction performance. Then, by aggressive pruning of less informative nucleotide sites, while maintaining the high sensitivity of the method, we improved the model's performance in terms of the computational speed. In addition, after pruning fewer nucleotide sites need to be tagged, which in turn simplifies the development of an assay. The proposed method was tested on the human splice dataset. The results indicate that the proposed method was successful at splice site prediction with optimal sensitivity.