Boosting classifier for predicting protein domain structural class |
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Authors: | Feng Kai-Yan Cai Yu-Dong Chou Kuo-Chen |
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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 |
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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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Keywords: | Domain structural classification Binary LogitBoost One-vs-others LogitBoost AdaBoost Support vector machines Neural network |
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