Finding Genes in DNA using Machine Learning Algorithms

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The automatic and efficient recognition of genes in DNA sequences is now widely recognized as a central problem in computational molecular biology. This research project uses the gene recognition problem in databases of gene sequence data as a driver for research in machine learning. It builds on several promising recent results, investigating decision tree methods, memory-based reasoning, and hidden Markov models. Each of these approaches has particular strengths. Recent research by the PI has found that decision trees can achieve consistently higher accuracy that any previous methods for finding protein coding regions in very short DNA subsequences. Embedding of the decision tree approach in a dynamic programming algorithm can be used to produce a more general system. The techniques discovered to be most effective will be incorporated into a complete gene finding system that can identify genes with high accuracy, augmented with a user-friendly interface, and made freely available to the biology and computer science research communities.