Prediction Of Protein Structure Based On The Combination Of Multiple Classifiers

The success of human genome project makes the number of protein sequences entering into data bank rapidly increasing. Theoretical method computing for predict -ing the structure and function of protein and guiding the experiments is very significative work. In this thesis, we use several methods of multiple classifiers combination to classify protein structures based on the protein primary sequences. The main work is summarized as follow:1. The knowledge about protein is introduced firstly. During researching about the predicting the structure of protein, the methods of multiple classifiers combination are applied to classify the structure of protein. And we summarized the methods about multiple classifiers combination and the classifiers such as support vector machine based on the researches of lots of scholars.2. Researching about multi-class protein fold recognition, we use the cascade algorithms based on support vector machine to classify the folds. The total accuracy is nearly 4 percentile higher than direct-classifying. This result suggests the thought is feasible.3. We investigate the theoretical framework of multiple classifiers fusion, and apply the decision template algorithms to classify the protein secondary structural classes. Then three kinds of improving algorithms are proposed. We use the different experiments to validate them. The results of the experiments are better. It shows that the algorithms and the thought of using multiple classifiers fusion to solve classifying the protein structural classes are effective.4. Researching the theoretical framework for classifier selection, we explain the algorithms of dynamic classifier selection using local class accuracy estimates and that of clustering-and-selection and apply them to classify the homo-oligomeric of the protein. We compare with the prediction using support vector machine. The result of the experiments suggests that the precision and the reliability using the selection algorithms are better than those of using support vector machine.This thesis is endowed by the postgraduate carving out seed foundation of Northwestern Polytechnical University, No.Z20030048.