Soybean Genome Clustering Using Quantum-Based Fuzzy C-Means Algorithm

Bioinformatics is a new area of research in which many computer scientists are working to extract some useful information from genome sequences in a very less time, whereas traditional methods may take years to fetch this. One of the studies that belongs to the area of Bioinformatics is protein sequence analysis. In this study, we have considered the soybean protein sequence which does not have class information therefore clustering of these sequences is required. As these sequences are very complex and consist of overlapping sequences, therefore Fuzzy C-Means algorithm may work better than crisp clustering. However, the clustering of these sequences is a very time-consuming process also the results are not up to the mark by using existing crisp and fuzzy clustering algorithms. Therefore we propose here a quantum Fuzzy c-Means algorithm that uses the quantum computing concept to represent the dataset in the quantum form. The proposed approach also use the quantum superposition concept which fastens the process and also gives better result than the FCM algorithm.