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BOOSTING ASSOCIATIVE CLASSIFIER FOR PROTEIN SUBCELLULAR LOCALIZATION

Proteins play different roles in the body, and the task of understanding proteins function is very important in biological processes. Proteins are composed of linear sequences of smaller molecules called amino acids that accomplish most of the functions of the living cell. The goals in cell biology are to identify the subcellular locations of proteins. A number of different mechanisms have been used for prediction of subcellular locations of proteins. The protein may exist in more than one subcellular location. Predicting protein subcellular localization the traditional classifiers can only handle single label problems as well as suffer from low coverage for test protein. Our approach to develop new methods for better accuracy and the coverage of test protein to deal with multilabel classification. In this study we apply machine learning techniques such as Association rules, boosting techniques and classifier model. To generate the associationrule system divides a protein sequence into short k-mer sequence fragments. A large number of class association rules are mined from the protein sequence examples. Then, a boosting algorithm is applied to filter out strong rules to build up a final classifier. The reported test results depict the applicability, the classification performance and the test coverage of the our method.