Protein secondary structure prediction using neural network and simulated annealing algorithm
Predicting the secondary structure of a protein (alpha-helix, beta-sheet, coil) is an important step towards elucidating its three dimensional structure, as well as its function. In this research we use a multi-layer feed forward neural network for protein secondary structure prediction. The RS126 data set was used for training and testing the proposed neural network. We combined neural network and Simulated Annealing (SA) to further improve on the accuracy of protein secondary structure prediction. The results obtained show that by combining the neural network with SA technique improves the prediction accuracy in the range of 2-3%.
Neural network, Protein structure prediction, Rs126 data set, Simulated annealing
Akkaladevi, Somasheker, Ajay K. Katangur, Saeid Belkasim, and Yi Pan. "Protein secondary structure prediction using neural network and simulated annealing algorithm." In The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, pp. 2987-2990. IEEE, 2004.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings