Protein feature classification using particle swarm optimization and artificial neural networks
A protein superfamily consists of proteins which share amino acid sequence homology and are therefore functionally and structurally related. Protein classification focuses on predicting the function or the structure of new proteins. This can be done by classifying a new protein to a given family with previously known characteristics. Artificial neural networks have been successfully applied to problems in pattern classification, function approximation, and associative memories. The traditional Backpropagation (BP) algorithm is generally used to train multilayer feedforward network but they are limited to search for a suitable set of weights in an apriori fixed network topology. This mandates the selection of an appropriate optimized synaptic weight for the learning problem in hand. Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which is very effective in solving real valued global optimization problems. Thus, a hybrid method combining PSO-BP is implemented in this paper. PSO has the limitation of getting trapped in local minima. So, mutation of few particles are done based on probability of mutation and thus, a modified PSO is implemented. The main objective of the paper is to develop an efficient classifier using feedforward neural network. The efficiency is measured in terms of speed, predictive accuracy, sensitivity, and specificity.