String transformation-based Bayesian classification or proteins

Authors:
Timothy Meekhof;Gary W. Daughdrill;Robert B. Heckendorn
Affiliations:
University of Idaho, Moscow, Idaho;University of Idaho, Moscow, Idaho;University of Idaho, Moscow, Idaho
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

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Abstract

We describe a Markov chain Bayesian classification tool, SCS, that can perform data-driven classification of proteins and protein segments. Training data for interesting classification problems is often limited; thus, SCS uses string transformation functions to change the encoding of proteins to reduce problem perplexity and improve classification. A wrapper-based genetic algorithm is used to search the space of possible string transformation functions to find functions that improve classification.