Improving splice-junctions classification employing a novel encoding schema and decision-tree

Splice junctions are important regions in genes, which have been studied in many studies in Genetics. Recently some attempts in computer science have been made to use computer power in distinguishing the different splice junctions and non-junctions regions in genes. Ambiguity in the measurements of nucleotides is an important issue in dealing with these regions. In this paper a novel method is proposed along with an encoding schema which take ambiguities into account using probabilistic intuitions. The method is based on Decision Trees, using K Nearest Negihbours and Support Vector Machines. The results have shown the significance of using the proposed encoding schema and classification method.