title: Improved method for predicting protein fold patterns  with ensemble classifiers.
abstract: Protein folding is recognized as a critical problem in  the field of biophysics in the 21st century. Predicting protein-folding  patterns is challenging due to the complex structure of proteins. In an attempt  to solve this problem, we employed ensemble classifiers to improve prediction  accuracy. In our experiments, 188-dimensional features were extracted based on  the composition and physical-chemical property of proteins and 20-dimensional  features were selected using a coupled position-specific scoring matrix.  Compared with traditional prediction methods, these methods were superior in  terms of prediction accuracy. The 188-dimensional feature-based method achieved  71.2% accuracy in five cross-validations. The accuracy rose to 77% when we used  a 20-dimensional feature vector. These methods were used on recent data, with  54.2% accuracy. Source codes and dataset, together with web server and software  tools for prediction, are available at:  http://datamining.xmu.edu.cn/main/~cwc/ProteinPredict.html.
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, Fujian, China.
authors.forename: W
authors.initials: W
authors.lastname: Chen
authors.forename: X
authors.initials: X
authors.lastname: Liu
authors.forename: Y
authors.initials: Y
authors.lastname: Huang
authors.forename: Y
authors.initials: Y
authors.lastname: Jiang
authors.forename: Q
authors.initials: Q
authors.lastname: Zou
authors.forename: C
authors.initials: C
authors.lastname: Lin
mesh_terms: D030562:Databases, Protein; D010363:Pattern Recognition, Automated / Q000379:methods; D017510:Protein Folding; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics; D020539:Sequence Analysis, Protein; D012984:Software
chemical_list: D011506:Proteins