GA-Facilitated Knowledge Discovery and Pattern Recognition Optimization Applied to the Biochemistry of Protein Solvation

Authors

Michael R. Peterson, Wright State University - Main Campus
Travis E. Doom, Wright State University - Main CampusFollow
Michael L. Raymer, Wright State University - Main CampusFollow

Document Type

Conference Proceeding

Publication Date

6-2004

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Abstract

The authors present a GA optimization technique for cosine-based k-nearest neighbors classification that improves predictive accuracy in a class-balanced manner while simultaneously enabling knowledge discovery. The GA performs feature selection and extraction by searching for feature weights and offsets maximizing cosine classifier performance. GA-selected feature weights determine the relevance of each feature to the classification task. This hybrid GA/classifier provides insight to a notoriously difficult problem in molecular biology, the correct treatment of water molecules mediating ligand binding to proteins. In distinguishing patterns of water conservation and displacement, this method achieves higher accuracy than previous techniques. The data mining capabilities of the hybrid system improve the understanding of the physical and chemical determinants governing favored protein-water binding.

Comments

Presented at the Genetic and Evolutionary Computation Conference, Seattle, WA, June 26-30, 2004.

Repository Citation

Peterson, M. R., Doom, T. E., & Raymer, M. L. (2004). GA-Facilitated Knowledge Discovery and Pattern Recognition Optimization Applied to the Biochemistry of Protein Solvation. Lecture Notes in Computer Science, 3102, 426-437.
https://corescholar.libraries.wright.edu/knoesis/920

DOI

10.1007/978-3-540-24854-5_43