Qianwu Ni and Lei Chen* Pages 612 - 621 ( 10 )
Aim and Objective: Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification.
Material and Methods: In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model.
Results: Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure.
Conclusion: The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance.
Protein structural class prediction, minimum redundancy maximum relevance, feature selection, algorithm selection, ensemble classifier, optimal ensemble prediction model.
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, College of Information Engineering, Shanghai Maritime University, Shanghai 201306