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Pattern Classification Using NNTree: Design and Application for Biological Dataset

Recent advancement and wide use of high-throughput technologies for biological research are producing enormous size of biological datasets distributed worldwide. Pattern classification techniques and machine learning methods provide useful tools for knowledge discovery in this field. The goal of this paper is to present the design and application of a neural network tree (NNTree) based pattern classifier to mine biological datasets. An NNTree is a hybrid-learning model with the overall structure being a decision tree (DT), and each non-terminal node containing a neural network (NN). Demon-strating its success in splice-junction and gene identification problems provides the effectiveness of this approach. Extensive experimental results establish that the proposed classifier produces more accurate classifier than that have previously been obtained for a range of different sequence lengths; thereby indicating a cost-effective alternative in splice-junction and protein coding region identification problem.