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The Algorithm of implementation for genome analysis ecosystems : Mitochondria`s case
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  • Journal title : Journal of Digital Convergence
  • Volume 14, Issue 4,  2016, pp.349-353
  • Publisher : The Society of Digital Policy and Management
  • DOI : 10.14400/JDC.2016.14.4.349
 Title & Authors
The Algorithm of implementation for genome analysis ecosystems : Mitochondria`s case
Choi, Sung-Ja; Cho, Han-Wook;
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The studies on the human environment and ecosystem analysis is being actively researched. In recent years, The service of genome analysis has been offering the customized service to prevent the disease as reading an individual`s genome information. The genome information by analyzing technology is being required accurate and fast analyses of ecosystem-dielectrics due to the spread of the disease, the use of genetically modified organism and the influx of exotic. In this paper the algorithm of K-Mean clustering for a new classification system was utilized. It will provide new dielectrics information as quickly and accurately for many biologists.
Bio Informatics;Clustering;K-Mean;Genomics;Health care;
 Cited by
Keun-Ho Lee, "A Method of Defense and Security Threats in U-Healthcare Service", Journal of the Korea Convergence Society, Vol. 3, No. 4, pp. 1-5, 2012.

Eun-Hee Park, Hye-Suk Kim, Ja-Ok Kim, "The Effect of Convergence Action Learning techniques in Simulation Class", Journal of the Korea Convergence Society, Vol. 6, No. 5, pp. 241-248, 2015.

P. Tamayo et al., Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation, PNAS 96: 2907-12, 1999. crossref(new window)

A. Alizadeh et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature 403: 503-11, 2000. crossref(new window)

E. Furlong et al., Patterns of Gene Expression During Drosophila Development, Science 293: 1629-33, 2001. crossref(new window)

Evanno, Guillaume, Sebastien Regnaut, and Jerome Goudet. "Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study." Molecular ecology 14.8 (2005): 2611-2620. crossref(new window)

Oja, Merja, Samuel Kaski, and Teuvo Kohonen. "Bibliography of self-organizing map (SOM) papers: 1998-2001 addendum." Neural computing surveys 3.1 (2003): 1-156.

Wang, K. et al. Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray. Gene 229, 101-108 (1999). crossref(new window)

Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863-14868 (1998). crossref(new window)

Nakamura, Yasukazu, Takashi Gojobori, and Toshimichi Ikemura. "Codon usage tabulated from international DNA sequence databases: status for the year 2000." Nucleic acids research 28.1 (2000): 292-292. crossref(new window)

Crick, Francis HC. "Codon-anticodon pairing: the wobble hypothesis." Journal of molecular biology 19.2 (1966): 548-555. crossref(new window)

Ikemura, Toshimichi. "Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system." Journal of molecular biology 151.3 (1981): 389-409. crossref(new window)

Wellmer, Frank, and Jose Luis Riechmann. "Gene network analysis in plant development by genomic technologies." International Journal of Developmental Biology 49.5/6 (2005): 745. crossref(new window)

Jobson, J. (1992) Applied Multivariate Data Analysis: Categorical and Multivariate Methods (Springer, NewYork).

Hartigan, J. (1975) Clustering Algorithms (Wiley, New York).

Gordon, A. E. (1981) Classification: Methods for the Exploratory Analysis of Multivariate Data (Chapman & Hall, New York).