Boosting classifier for predicting protein domain structural class.

Kai-Yan Feng; Yu-Dong Cai; Kuo-Chen Chou

doi:10.1016/j.bbrc.2005.06.075

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Boosting classifier for predicting protein domain structural class.

Kai-Yan Feng·Yu-Dong Cai·Kuo-Chen Chou

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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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Algorithms Amino Acid Sequence Artificial Intelligence Computer Simulation Models, Chemical Models, Molecular Molecular Sequence Data Protein Conformation Protein Structure, Tertiary Proteins Sequence Alignment Sequence Analysis, Protein Structure-Activity Relationship Evaluation Study Journal Article

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Kai-Yan Feng, Yu-Dong Cai, Kuo-Chen Chou. 2005-08-19. Boosting classifier for predicting protein domain structural class.. https://doi.org/10.1016/j.bbrc.2005.06.075

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