Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10769494 | Biochemical and Biophysical Research Communications | 2005 | 5 Pages |
Abstract
A novel classifier, the so-called “LogitBoost” classifier, was introduced to predict the structural class of a protein domain according to its amino acid sequence. LogitBoost is featured by introducing a log-likelihood loss function to reduce the sensitivity to noise and outliers, as well as by performing classification via combining many weak classifiers together to build up a very strong and robust classifier. It was demonstrated thru jackknife cross-validation tests that LogitBoost outperformed other classifiers including “support vector machine,” a very powerful classifier widely used in biological literatures. It is anticipated that LogitBoost can also become a useful vehicle in classifying other attributes of proteins according to their sequences, such as subcellular localization and enzyme family class, among many others.
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Authors
Kai-Yan Feng, Yu-Dong Cai, Kuo-Chen Chou,