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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Cancer diagnosis,proteomics,Molecular biology,distributed clustering
- چکیده:
- چکیده انگلیسی: Objective: With the over saturating growth of biological sequence databases, handling of these amounts of data has
increasingly become a problem. Clustering has become one of the principal research objectives in structural and functional
genomics. However, exact clustering algorithms, such as partitioned and hierarchical clustering, scale relatively poorly
in terms of run time and memory usage with large sets of sequences. Methods: From these performance limits, heuristic
optimizations such as Cuckoo Search Algorithm with genetic operators (ICSA) algorithm have been implemented in
distributed computing environment. The proposed ICSA, a global optimized algorithm that can cluster large numbers
of protein sequences by running on distributed computing hardware. Results: It allocates both memory and computing
resources efficiently. Compare with the latest research results, our method requires only 15% of the execution time and
obtains even higher quality information of protein sequence. Conclusion: From the experimental analysis, We noticed
that the cluster of large protein sequence data sets using ICSA technique instead of only alignment methods reduce
extremely the execution time and improve the efficiency of this important task in molecular biology. Moreover, the
new era of proteomics is providing us with extensive knowledge of mutations and other alterations in cancer study.- انتشار مقاله: 30-09-1396
- نویسندگان: Thenmozhi K,Karthikeyani Visalakshi N,Shanthi S,Pyingkodi M
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: Protein Sequence Motifs,Cancer Detection,Distribution Based Spectral Clustering,Generalized Jaccard Similarity,Soft Computing
- چکیده:
- چکیده انگلیسی: 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.- انتشار مقاله: 01-12-1396
- نویسندگان: Thenmozhi K,Karthikeyani Visalakshi N,Shanthi S
- مشاهده