Distribution based Fuzzy Estimate Spectral Clustering for Cancer Detection with Protein Sequence and Structural Motifs

Abstract

Objective: In biological data analysis, protein sequence and structural motifs are an amino-acid sequence patterns<br />that are widespread and used as tools for detecting the cancer at an earlier stage. To improve the cancer detection with<br />minimum space and time complexity, Distribution based Fuzzy Estimate Spectral Clustering (DFESC) technique is<br />developed. Methods: Initially, the protein sequence motifs are taken from dataset to form the cluster. The Distribution<br />based spectral clustering is applied to group the protein sequence by measuring the generalized jaccard similarity<br />between each protein sequences. To develop the clustering accuracy, soft computing technique namely fuzzy logic is<br />applied to calculate membership value of each sequence motifs. Results: The outcome showed that the presented DFESC<br />technique effectively identifies the cancer in terms of clustering accuracy, false positive rate, and cancer detection time<br />and space complexity. Conclusion: Based on the observations, evaluation of DFESC technique provides improved<br />result for premature detection of cancer using protein sequence and structural motifs.