Prediction of relative solvent accessibility by support vector regression and best-first method

Since, it is believed that the native structure of most proteins is defined by their sequences, utilizing data mining methods to extract hidden knowledge from protein sequences, are unavoidable. A major difficulty in mining bioinformatics data is due to the size of the datasets which contain frequently large numbers of variables. In this study, a two-step procedure for prediction of relative solvent accessibility of proteins is presented. In a first "feature selection" step, a small subset of evolutionary information is identified on the basis of selected physicochemical properties. In the second step, support vector regression is used to real value prediction of protein solvent accessibility with these custom selected features of evolutionary information. The experiment results show that the proposed method is an improvement in average prediction accuracy and training time.