Title: An Effective Feature Selection Optimization Algorithm For Gene Splice Junction Sites Prediction
Abstract: Objective Splice junction sites are the boundaries between exons and introns in eukaryotic gene sequences. If the splice junction sites in a gene sequence could be predict correctly, the coding regions in a gene would be separated from the non-coding regions. Method This paper proposes a machine learning approach for the modeling and prediction of splice junction sites by an effective feature selection algorithm. This algorithm selects every pair of parent and child node in original chain models for splice junction sites as features, and then uses genetic algorithm and a MAP(Maximum A Posteriori) classifier to select the features. Results and Conclusion The experiment result shows that the new algorithm can optimize the chain models and improve the prediction accuracy of splice junction sites. Besides the architecture of the optimized chain models can also help to understand the procedures of gene translation and expression in eukaryotic cells.
Publication Year: 2005
Publication Date: 2005-01-01
Language: en
Type: article
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