Using supervised fuzzy clustering to predict protein structural classes.
Prediction of protein classification is both an important and a tempting topic in protein science. This is because of not only that the knowledge thus obtained can provide useful information about the overall structure of a query protein, but also that the practice itself can technically stimulate the development of novel predictors that may be straightforwardly applied to many other relevant areas. In this paper, a novel approach, the so-called "supervised fuzzy clustering approach" is introduced that is featured by utilizing the class label information during the training process. Based on such an approach, a set of "if-then" fuzzy rules for predicting the protein structural classes are extracted from a training dataset. It has been demonstrated through two different working datasets that the overall success prediction rates obtained by the supervised fuzzy clustering approach are all higher than those by the unsupervised fuzzy c-means introduced by the previous investigators [C.T. Zhang, K.C. Chou, G.M. Maggiora. Protein Eng. (1995) 8, 425-435]. It is anticipated that the current predictor may play an important complementary role to other existing predictors in this area to further strengthen the power in predicting the structural classes of proteins and their other characteristic attributes.
Shen
Hong-Bin
HB
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China.
Yang
Jie
J
Liu
Xiao-Jun
XJ
Chou
Kuo-Chen
KC
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D017143:Fuzzy Logic; D008956:Models, Chemical; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid; D013329:Structure-Activity Relationship
D011506:Proteins