Predicting Splice Site by Improved Bayesian Classifier

Due to the enormous amount of DNA sequences to be processed, the computational speed is an important issue to be considered. Although relatively high accuracy has been achieved by existing methods, most of these prediction methods are computationally intensive. In this paper, a novel method for predicting DNA splice sites using improved bayesian classifier is presented. Naive bayesian classifier is a simple and effective classification method. Combined with least square, the dependence among attributes which assumed to be independent originally can be expressed using linear function. This improves the classification performance. The simulation results show the computation time is linear to the number of sequences tested, while the performance is notably improved compared with the naive bayesian classifier. The classification results of the proposed method are also comparable to the solution quality obtained by the existing discovery tools, while the speed of the proposed method is significantly faster. This is a notable improvement in computational modeling considering the huge amount of DNA sequences to be processed.