Multi-class protein fold recognition using adaptive codes

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 PSIBLAST methods on every structure recognition problem we consider.

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Eugene Ie, Jason Weston, William Stafford Noble, Christina S. Leslie
• Keywords: Fold Recognition Problem, ICML 2005, Machine Learning, Multi-class Classification Method, Protein Structural Hierarchy