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Boosting classifier for predicting protein domain structural class
Authors:Feng Kai-Yan  Cai Yu-Dong  Chou Kuo-Chen
Affiliation:a Imaging Science and Biomedical Engineering, Medical School, The University of Manchester, Manchester, M13 9PT, UK
b Biomolecular Sciences Department, University of Manchester Institute of Science and Technology, Post Box 88, Manchester, M60 1QD, UK
c Gordon Life Science Institute, 13784 Torrey Del Mar, San Diego, CA 92130, USA
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.
Keywords:Domain structural classification   Binary LogitBoost   One-vs-others LogitBoost   AdaBoost   Support vector machines   Neural network
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