Combining GOR techniques with support vector machines for protein secondary structure prediction
We propose a novel approach to predict protein secondary structure by combining different types of GOR (Garnier, Osguthorpe, and Robson) classifiers with Support Vector Machines (SVMs). The new prediction scheme achieves an accuracy of 69.3% when using the sevenfold cross validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins significantly increases the prediction accuracy to 72.1%. We show that it is possible to obtain a higher accuracy with combined classifiers than GOR classifiers or Support Vector Machines alone, in protein secondary structure prediction.