Multi-Class Protein Fold Recognition Using Adaptive Codes
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.
Cite
Text
Ie et al. "Multi-Class Protein Fold Recognition Using Adaptive Codes." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102393Markdown
[Ie et al. "Multi-Class Protein Fold Recognition Using Adaptive Codes." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/ie2005icml-multi/) doi:10.1145/1102351.1102393BibTeX
@inproceedings{ie2005icml-multi,
title = {{Multi-Class Protein Fold Recognition Using Adaptive Codes}},
author = {Ie, Eugene and Weston, Jason and Noble, William Stafford and Leslie, Christina S.},
booktitle = {International Conference on Machine Learning},
year = {2005},
pages = {329-336},
doi = {10.1145/1102351.1102393},
url = {https://mlanthology.org/icml/2005/ie2005icml-multi/}
}