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Reseach Article

A Grid based Mining Approach to Genomic Data Set

by S. Jessica Saritha, P.govindarajulu
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 4
Year of Publication: 2015
Authors: S. Jessica Saritha, P.govindarajulu
10.5120/20321-2398

S. Jessica Saritha, P.govindarajulu . A Grid based Mining Approach to Genomic Data Set. International Journal of Computer Applications. 116, 4 ( April 2015), 1-7. DOI=10.5120/20321-2398

@article{ 10.5120/20321-2398,
author = { S. Jessica Saritha, P.govindarajulu },
title = { A Grid based Mining Approach to Genomic Data Set },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 4 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number4/20321-2398/ },
doi = { 10.5120/20321-2398 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:56:42.209834+05:30
%A S. Jessica Saritha
%A P.govindarajulu
%T A Grid based Mining Approach to Genomic Data Set
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 4
%P 1-7
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

An improvement to the processing efficiency of genomic data sequence for automated detection and diagnosis is presented in this paper. For the automation of genomic signal processing, the problem of representation, extraction and retrieval is proposed. In the current form of automated genomic processing system, the retrieval of the gene information depends on the representation of the gene sequence. The retrieval accuracy also depends on the training data sets used. To achieve e the accuracy of retrieval it is required to represent the informatics regions more accurately and extract the relevant matching faster. To achieve this objective in this paper, a grid based computing approach to the distribution genomic dataset is proposed, with sequence shuffled, information region prediction filtration. A faster and accurate retrieval is obtained by the usage of sequencing, filtration and grdification modeling.

References
  1. Gir Won Lee ,Sangsoo Kim, "Genome data mining for everyone", BMB Reports, Minireview, 2008.
  2. Doug Szajda, Michael Pohl, Jason Owen, Barry Lawson, "Toward A Practical Data Privacy Scheme for A Distributed Implementation of the Smith-Waterman Genome Sequence Comparison Algorithm", NSF 2000.
  3. Georgios A Pavlopoulos, AnastasisOulas, Ernesto Iacucci, Alejandro Sifrim, Yves Moreau, Reinhard Schneider, Jan Aerts and IoannisIliopoulos, "Unraveling genomic variation from next generation sequencing data", BioData Mining 2013.
  4. Anton James Enright, "Computational Analysis of Protein Function within Complete Genomes", thesis, 2002.
  5. Peter Schattner, "Genomics made easier: An introductory tutorial to genome data mining", Genomics 93, Elsvier, 2009.
  6. Inbamalar T M, Sivakumar R, "Filtering Approach to DNA Signal Processing", IACSIT Conference, 2012.
  7. Yukinori Okada, Robert M. Plenge, "Entering the Age of Whole-Exome Sequencing in Rheumatic Diseases: Novel Insights into Disease Pathogenicity", Arthritis & Rheumatism, 2013,
  8. Anastassiou D, "Frequency-domain analysis of bio-molecular sequences", Bioinformatics, Vol. 16, No. 12, 2000.
  9. Anastassiou D, "Genomic Signal Processing", IEEE Signal Processing Magazine, July, 2001.
  10. P. P. Vaidyanathan ,Byung-Jun Yoon, "The role of signal-processing concepts in genomics and proteomics", Genomics, 2004.
  11. ZhengGuo, Tianwen Zhang, Xia Li, Qi Wang, JianzhenXu, Hui Yu, Jing Zhu, Haiyun Wang, Chenguang Wang, Eric J Topol, Qing Wang and ShaoqiRao, "Towards precise classification of cancers based on robust gene functional expression profiles", BMC Bioinformatics 2005.
  12. Julie A. Hawkins, Colin E. Hughes, Robert W. Scotland, "Primary Homology Assessment, Characters and Character States", Cladistics 13, 1997.
  13. P. Vivekanandan, R. Nedunchezhian, "A New Incremental Genetic Algorithm Based Classification Model To Mine Data With Concept Drift", JATIT, 2010.
  14. Paivi Onkamo1,HannuToivonen, "A survey of data mining methods for linkage disequilibrium mapping", Human Genomics, 2006.
  15. S. Vidhya, S. Karthikeyan, "A Security Based Data Mining Approach In Data Grid", Journal Of Computing, 2010.
  16. Mahmood Akhtar, "Comparison of Gene and Exon Prediction Techniques for Detection of Short Coding Regions", IJIT 2005.
  17. Shreyas Sen, Seetharam Narasimhan, Amit Konar, "Biological Data Mining for Genomic Clustering Using Unsupervised Neural Learning", Engineering Letters, 2007.
  18. Dominik Grimm, "Data Mining in Bioinformatics Day 6: Classification in Next Generation Sequencing Data Analysis", 2013.
  19. NavinChatlani, John J. Soraghan, "Local Binary Patterns For 1-D Signal Processing", EUSIPCO-2010.
  20. JianfengRen, "Noise-Resistant Local Binary Pattern With an Embedded Error-Correction Mechanism", IEEE Transactions On Image Processing, 2013.
  21. M. Ben Haj Hmida, Y. Slimani, "High performance Grid computing for detecting gene-gene interactions in genome-wide association studies", geonomics 2000.
  22. Jiang, D. , Pei, J. and Zhang, A. . DHC: A Density-based Hierarchical Clustering Method for Time- series Gene Expression Data. In Proceeding of BIBE2003: 3rd IEEE International Symposium on Bioinformatics and Bioengineering, Bethesda, Maryland, March 10-12 2003.
  23. Edward B. Suh, "Parallel Computing Methods for Analyzing Gene Expression Relationships", SPIE 2001.
  24. http://www. ncbi. nlm. nih. gov/nuccore/M17262. 1
Index Terms

Computer Science
Information Sciences

Keywords

Data mining genomic signal processing spectral sequencing region prediction grid computing.