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

Document Type: Research Articles

Authors

1 Department of Computer Applications, Selvam College of Technology, Namakkal, TamilNadu, India.

2 Department of Computer Science, Government Arts and Science College, Kangayam, TamilNadu, India.

3 Department of Computer Applications, Kongu Engineering College, Erode, TamilNadu, India.

Abstract

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

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