Boosting classifier for predicting protein domain structural class.

نویسندگان

  • Kai-Yan Feng
  • Yu-Dong Cai
  • Kuo-Chen Chou
چکیده

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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عنوان ژورنال:
  • Biochemical and biophysical research communications

دوره 334 1  شماره 

صفحات  -

تاریخ انتشار 2005