Biological sequence data preprocessing for classification: A case study in splice site identification

Abstract

The increasing growth of biological sequence data demands better and efficient analysis methods. Effective detection of various regulatory signals in these sequences requires the knowledge of characteristics, dependencies, and relationship of nucleotides in the surrounding region of the regulatory signals. A higher order Markov model is generally regarded as a useful technique for modeling higher order dependencies of the nucleotides. However, its implementation requires estimating a large number of computationally expensive parameters. In this paper, we propose a hybrid method consisting of a first order Markov model for sequence data preprocessing and a multilayer perceptron neural network..