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Reseach Article

Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data

by Rajbir Singh, Neha Garg, Dheeraj Pal Kaur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 6
Year of Publication: 2014
Authors: Rajbir Singh, Neha Garg, Dheeraj Pal Kaur
10.5120/17530-8103

Rajbir Singh, Neha Garg, Dheeraj Pal Kaur . Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data. International Journal of Computer Applications. 100, 6 ( August 2014), 31-36. DOI=10.5120/17530-8103

@article{ 10.5120/17530-8103,
author = { Rajbir Singh, Neha Garg, Dheeraj Pal Kaur },
title = { Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 6 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume100/number6/17530-8103/ },
doi = { 10.5120/17530-8103 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:30:27.507642+05:30
%A Rajbir Singh
%A Neha Garg
%A Dheeraj Pal Kaur
%T Analyzing Gene Expressions in Saccharomyces Cerevisiae using Hierarchical Clustering of DNA Microarray Data
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 6
%P 31-36
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Bioinformatics is a data intensive field of research and development. DNA microarray used to better understand form of saccharomyces cerevisiae disease such as cancer. Microarray allows us to diagnose and treat patients more successfully. Statistical method devoted to detection in DNA from microarray data, the inherent challenges in data quality associated with most filter techniques remains a challenging problem in microarray association studies. Applying methods of simulation studies and a genome-wide association microarray study in saccharomyces cerevisiae, that find current approach significantly improve DNA microarray cell reduces the yeast value rates and false positive genes variation. Clustering is the one of the main techniques for data mining. Microarray is the evolutionary history for a set of evolutionary related genes expression data. There are number of different distance based methods of which two are dealt with here: Euclidean method and Manhattan method. . A method for construction of distance based gens expression using clustering is proposed and implemented on different saccharomyces cerevisiae samples. Evolutionary distances between two or more genes are calculated using p-distance method. Multiple samples are applied on different datasets. Hierarchical clustering and k-mean clustering are constructed for different datasets from available data using both the distance based methods. Then, final cluster is constructed using these closely related filter dataset.

References
  1. A. Istvan, Travis N. (2007) "Mavrich Translational and rotational settings of H2A. Z nucleosomes across the Saccharomyces cerevisiae genome", Nature 446, 572-576.
  2. Catalina Martínez-Costa, Marcos Menárguez Tortosa (2009) "A model-driven approach for representing clinical archetypes for Semantic Wwb environments", JBI, vol. 42, pp. 150-164.
  3. D. Mark, D. Robinson (2010) "Bioconductor package for differential expression analysis of digital gene expression data", vol. 26, pp. 139-140.
  4. F. Jihua , D. Xianhua (2010) "A simulation model for nucleosome distribution in the yeast genome based on integrated cross-platform positioning datasets", Mathematical and Computer Modeling, PR China, Vol. 52, pp. 1932-1939.
  5. K. Archi and Dr. S. Amardeep (2013) "Implementing Phylogenetic Distance Based Methods for Tree Construction Using Hierarchical Clustering", IJCSET 2013, Vol. 4, No. 07, pp. 54-61.
  6. L. Kaufman and P. J. Rousseeuw (2005) "Finding Groups in Data: an Introduction to Cluster Analysis", 1sted. , John Wiley and Sons.
  7. R. Jen-hwa Chu1 Angela (2013) "Copy number variation genotyping using family information", vol. 14, pp. 157-168.
  8. V. Heroen (2010) "Data mining approach identifies research priorities and data requirements for resolving the red algal tree of life", BMC Evolutionary Biology, doi: 10. 1186/1471-2148-10-16.
  9. O. Frick (2005) "Characterization of the metabolic shift between oxidative and fermentative growth in Saccharomyces cerevisiae by comparative 13C flux analysis", Microb Cell Fact. Vol. no. 10, pp. 4-3.
  10. C. Harry (2011) "Implemented genome-wide methylation arrays has proved very informative to investigate both clinical and biological questions in human epigenomics", PMC, vol. 2, pp. 88.
  11. I. S. Kohane, A. T. Kho (2003) "Microarrays for an Integrative Genomics",Massachusetts, London, England, MIT Press Cambridge.
  12. U. Maulik and S. Bandyopadhyay (2000) "Genetic algorithm-based clustering technique",Pat-tern Recognition, vol. 33, pp. 1455–1465.
  13. G. J. McLachlan, R. W. Bean (2002) "A mixture model-based approach to the clustering of microarray expression data", Bioinformatics, vol. 18, pp. 413–422.
  14. D. Mark. Robinson (2010) "Bioconductor package for differential expression analysis of digital gene expression data", vol. 26, pp. 139-140.
  15. R. Shamir and R. Sharan, (2000) "A clustering algorithm for gene expression analysis", 8th International Conference on Intelligent Systems for Molecular Biology (ISMB 00), AAAI Press.
Index Terms

Computer Science
Information Sciences

Keywords

Saccharomyces cerevisiae samples Euclidean and Manhattan method Hierarchical clustering and K-mean clustering Profile levels Principal component.