Optimizing Big Data in Bioinformatics with Swarm Algorithms

This paper describes the application of swarm algorithms on bioinformatics data namely protein sequences. The big data that exists in bioinformatics domains require an intelligent method that capable to increase the performance of classification as well as discovering the knowledge. The work optimizes the big features that exist in protein sequences using the two-tier hybrid model by applying the filter and wrapper method. The use of swarm algorithm namely particle swarm optimization has improved the classification accuracy as the features are optimized prior to classification. The study also compares the performance of swarm algorithms with the standard searching method.