Protein Secondary Structure Prediction using Bayesian Inference method on Decision fusion algorithms

Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging task, and the problem has been approached from several angles. Previously research was performed in this field using several techniques such as neural networks, simulated annealing (SA) and genetic algorithms (GA) for improving the protein secondary structure prediction accuracy. Decision fusion methods such as the committee method and correlation methods were also used in combination with the profile-based neural networks and AI algorithms for achieving better prediction accuracy. In this research we investigate the Bayesian inference method for predicting the protein secondary structure. The Bayesian inference method proposed in this research uses the results from the committee and correlation methods to achieve better prediction accuracy. Simulations are performed using the RS126 data set. The results show that the protein secondary structure prediction accuracy can be improved by more than 2% using the Bayesian inference method.