PSSP with dynamic weighted kernel fusion based on SVM-PHGS
Since 1960s, researchers have proposed several prediction methods, for protein secondary structure prediction (PSSP), whereas the accuracy of them is no more than 80%. In this case, there is an urgent need to introduce a high accuracy prediction method. One learning method called support vector machines (SVMs) has shown comparable or better results than neural networks on bioinformatics applications. This research proposes a method based on SVM which has been improved by a new parallel multi class (PMC) method, parallel hierarchical grid search (PHGS), cross validation (CV) technique and weighted kernel fusion (WKF) method. The presented PHGS has been applied to regularize parameters of SVMs kernel function which have an important impact on the accuracy. Using a suitable input data and kernel function for a particular problem can improve the prediction results remarkably. Also to improve our method, Position Scoring Matrix (PSSM) profiles are used as the input information to it. The goals of this study are to calibrate kernel function parameters and fusion of different kernel functions result in order to determine protein secondary structure classes accurately. The right choice of a fusion method is an important issue in creating a supreme performance so we propose a dynamic weight allocation method based on a non-linear analysis system. The obtained classification accuracies of our method are 84.65% and 83.94% on RS126 and CB513 datasets respectively and they are very promising with regard to other classification methods in the literature for this problem. Also for evaluating our method behavior in comparison to other state of arts methods, an independent dataset is used. The results show that the comprehensibility of WKF based on SVM-PHGS is much better than other methods.
weighted kernel fusion protein secondary structure prediction support vector machines parallel hierarchical grid search machine learning approach