Multi-class protein fold recognition using adaptive codes

Authors:
Eugene Ie;Jason Weston;William Stafford Noble;Christina Leslie
Affiliations:
Columbia University, New York, NY;NEC Research Institute, Princeton, NJ;University of Washington, Seattle, WA;Columbia University, New York, NY
Venue:
ICML '05 Proceedings of the 22nd international conference on Machine learning
Year:
2005

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Abstract

We develop a novel multi-class classification method based on output codes for the problem of classifying a sequence of amino acids into one of many known protein structural classes, called folds. Our method learns relative weights between one-vs-all classifiers and encodes information about the protein structural hierarchy for multi-class prediction. Our code weighting approach significantly improves on the standard one-vs-all method for the fold recognition problem. In order to compare against widely used methods in protein sequence analysis, we also test nearest neighbor approaches based on the PSI-BLAST algorithm. Our code weight learning algorithm strongly outperforms these PSI-BLAST methods on every structure recognition problem we consider.