Literature Survey on the Prediction of Secondary Structure of Proteins Using Radial Basis Function Neural Networks (RBFNN) and Support Vector Machines (SVM)
The Protein structure prediction has been an active research area for the last 40 years or so. The technical progress in computational Molecular Biology during the last decades has contributed significantly to the progress we see today. The major goal of predicting Protein structures underpins the correct assumption that three dimensional structures confer protein function. The linear Amino Acid sequences must transform to nonlinear Secondary Structures and then to Tertiary and Quaternary Structures that are responsible for biological functions. Biological functions may remain similar or change in the related organisms through the evolutionary process. By considering the importance of the prediction of secondary structure of protein a detailed literature study of the same using Radial Basis Function Neural Networks (RBFNN) AND Support Vector Machines (SVM) has been reviewed in this paper.