An Efficient Way of Finding Optimal Path using Protein Data Set: Ant Colony Optimization with Rough Set Theory for Feature Selection

Bioinformatics is one of the emerging technologies which is played an important role in the field of biology. The molecular biology and Bioinformatics information are extracted from the protein data set which is used for analysing the different kind of biological information. The major challenges in the protein data set are larger in size, which increases the complexity during the further experimental process. The complexity of the system is reduced by hybridized Soft Computing techniques and Evolutionary Methods. Thus, in this paper proposed that optimal feature selection method for reducing the dimensionality of the protein feature set to improve the performance of the proposed system. Initially, the biological data are grouped into the clusters which is fall into the pre-processing step for removing the missing and unwanted data's. The cluster formation is done by Ant Colony with Rough Set Theory (ACRST) based feature selection process. The performance of the system is evaluated with the help of the existing algorithms such as wrapper method, Greedy Forward Selection, Particle Swarm Optimization, Scatter Search and the comparison is analyzed with the help of the accuracy, sensitivity and specificity.