Performance of Machine Learning Techniques in Protein Fold Recognition Problem

In protein fold recognition problem an effort is made to assign a fold to given proteins, this is of practical importance and has diverse application in the field of bioinformatics such as the discovery of new drugs, the individual implication of amino acid in a protein and bringing improvement in a specific protein function. In this paper, we have studied various machine learning techniques for protein fold recognition problem, and compared Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel and Multilayer Perceptron (MLP) on a number of measures like the recognition accuracy of protein fold, the 10-fold cross validation accuracies and Kappa statistics. These techniques are applied to the well known Structural Classification of Proteins (SCOP) dataset in extensive experimentations. In this study Multilayer Perceptron (MLP) shows better accuracy on single protein feature (C, S, H, P, V, Z) of the SCOP dataset as compared to Support Vector Machine (SVM). A plausible reason of the better performance of MLP is that it uses all the available data for classification where as the SVM model cannot exploit all the available data.