@article { author = {K, Thenmozhi and N, Karthikeyani Visalakshi and S, Shanthi and M, Pyingkodi}, title = {Distributed ICSA Clustering Approach for Large Scale Protein Sequences and Cancer Diagnosis}, journal = {Asian Pacific Journal of Cancer Prevention}, volume = {19}, number = {11}, pages = {3105-3109}, year = {2018}, publisher = {West Asia Organization for Cancer Prevention (WAOCP), APOCP's West Asia Chapter.}, issn = {1513-7368}, eissn = {2476-762X}, doi = {10.31557/APJCP.2018.19.11.3105}, abstract = {Objective: With the over saturating growth of biological sequence databases, handling of these amounts of data hasincreasingly become a problem. Clustering has become one of the principal research objectives in structural and functionalgenomics. However, exact clustering algorithms, such as partitioned and hierarchical clustering, scale relatively poorlyin terms of run time and memory usage with large sets of sequences. Methods: From these performance limits, heuristicoptimizations such as Cuckoo Search Algorithm with genetic operators (ICSA) algorithm have been implemented indistributed computing environment. The proposed ICSA, a global optimized algorithm that can cluster large numbersof protein sequences by running on distributed computing hardware. Results: It allocates both memory and computingresources efficiently. Compare with the latest research results, our method requires only 15% of the execution time andobtains even higher quality information of protein sequence. Conclusion: From the experimental analysis, We noticedthat the cluster of large protein sequence data sets using ICSA technique instead of only alignment methods reduceextremely the execution time and improve the efficiency of this important task in molecular biology. Moreover, thenew era of proteomics is providing us with extensive knowledge of mutations and other alterations in cancer study.}, keywords = {proteomics,Cancer diagnosis,Molecular biology,distributed clustering,Genetic Algorithm}, url = {https://journal.waocp.org/article_76633.html}, eprint = {https://journal.waocp.org/article_76633_4db512d7006290f47be7e0ee66220967.pdf} }