Protein Motif Comparator Using Bio-Inspired Two-Way K-Means

Abstract

In this scientific world, the evolution of the disease is predominantly higher than the medicines. The diagnosis and prognosis of such diseases will differ from patient to patient. In this scenario, the protein motifs are very useful for understanding the functionality and lethality of the disease. Most of the existing techniques are supervised approaches which require prior knowledge of the data. As the protein sequences are unsupervised data, the unsupervised data mining techniques like Clustering and 2-way Clustering are chosen to mine the homologous protein motifs. The quality of the results is refined further using the bio-inspired computing models like Particle Swarm Optimization, Genetic Algorithm and Venus Flytrap Optimization in this research work. The existing approaches can mine homologous patterns with structure similarity of 75 percent which is increased in this proposed approach. The results from these three different approaches show that the bio-inspired based 2-way Clustering approaches can mine more homologous motifs than the clustering approaches.