Protein Secondary Structure Prediction Using Support Vector Machines (SVMs)

Bioinformatics or computational biology is field of science in which biology, computer science and information technology merges into a single discipline. In modern computation biology, protein secondary structure prediction is a major problem. Secondary structure prediction is depends on its amino acid sequence. Current studies prefer machine learning techniques for classification and regression task. Recently many researchers used various data mining and machine learning tool for protein structure prediction. Our intention is to use model based (i.e., supervised learning) approach for protein secondary structure prediction and our objective is to enhance the prediction of 2D protein structure problem using advance machine learning techniques like, linear and non-linear support vector machine with different kernel functions. The datasets used for this problem are Protein Data Bank (PDB) sets, which is based on structural classification of protein (SCOP), RS126 and CB513.