Multi-class protein fold recognition using adaptive codes

Eugene Ie^a, Jason Weston^b, William Stafford Noble^c, Christina Leslie^a

 

^aCenter for Computational Learning Systems, Columbia University, New York, NY 10115, USA, ^bNEC Research Institute, Princeton, NJ 08540, USA, 
^cDepartment of Genome Sciences, Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA.

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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.

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This material is based upon work supported by the National Science Foundation under Grant No. 0312706.

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Contact: Christina Leslie

Results
PDF
Supplementary data and code (setup with ranking perceptron code)
Computational Biology Group

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