Boosting classifier for predicting protein domain structural class.
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.
Feng
Kai-Yan
KY
Imaging Science and Biomedical Engineering, Medical School, The University of Manchester, Manchester, M13 9PT, UK.
Cai
Yu-Dong
YD
Chou
Kuo-Chen
KC
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D011506:Proteins