136
Views
7
CrossRef citations to date
0
Altmetric
Articles

Gene Expression Data Clustering Using Variance-based Harmony Search Algorithm

&

REFERENCES

  • A. Mukhopadhyay , U. Maulik , and S. Bandyopadhyay , “Multiobjective evolutionary approach to fuzzy clustering of microarray data,” Anal. Biol. Data , pp. 303–28, Sept. 2007.
  • D. Jiang , C. Tang , and A. Zhang , “Cluster analysis for gene expression data: A survey,” IEEE Trans. Knowl. Data Eng. , Vol. 16, no. 11, pp. 1370–86, Nov. 2004.
  • V. Kumar , J. K. Chhabra , and D. Kumar , “Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems,” J. Comput. Sci. , Vol. 5, pp. 144–55, Mar. 2014.
  • P. Tamayo , D. Slonim , J. Mesirov , Q. Zhu , S. Kitareewan , E. Dmitrovsky , E. S. Lander , and T. R. Golub , “Interpreting patterns of gene expression with self organizing maps: Methods and application to hematopoietic differentiation,” in Proceedings of the National Academy of Sciences , USA , Vol. 96, 2007, pp. 2907–12.
  • A. K. Jain and R. C. Dubes , Algorithm for Clustering Data . Englewood Cliffs, NJ : Prentice-Hall, 1988.
  • D. E. Goldberg , Genetic Algorithms in Search, Optimization and Machine Learning . Boston, MA : Addison-Wesley, 1989.
  • E. Rashedi , H. N. Pour , and S. Saryazdi , “GSA: A gravitational search algorithm,” Inf. Sci. , Vol. 179, pp. 2232–48, Jun. 2009.
  • Y.-S. Ong , M. H. Lim , and X. Chen , “Research frontier: Memetic computation – past, present & future,” IEEE Comput. Intell. Mag. , Vol. 5, no. 2, pp. 24–36, Jan. 2010.
  • Z. W. Geem , J. Kim , and G. V. Loganathan , “A new heuristic optimization algorithm: Harmony search,” J. Simul. , Vol. 76, no. 2, pp. 60–8, Feb. 2001.
  • V. Kumar , J. K. Chhabra , and D. Kumar , “Variance-based harmony search algorithm for unimodal and multimodal optimization problems with application to clustering,” Cybernet. Syst.: Int. J. , Vol. 45, pp. 486–511, 2014.
  • D. Dembele and P. Kastner , “Fuzzy C-means for clustering microarray data,” Bioinformatics , Vol. 19, no. 8, pp. 973–80, 2003.
  • J. Oyelade , I. Isewon , F. Oladipupo , O. Aromolaran , E. Uwoghiren , F. Ameh , M. Achas , and E. Adebiyi , “Clustering algorithms: Their application to gene expression data,” Bio. Biol. Insights , Vol. 10, pp. 237–53, Nov. 2016.
  • A. Mukhopadhyay and U. Maulik , “Towards improving fuzzy clustering using support vector machine: Application to gene expression data,” Pattern Recognit. , Vol. 42, no. 11, pp. 2744–63, Nov. 2009.
  • M. B. Eisen , P. T. Spellman , P. O. Brown , and D. Botstein , “Cluster analysis and display of genome-wide expression patterns,” in Proceedings of National Academic Science , USA , 1998, pp. 14863 –85.
  • P. Langfelder , B. Zhang , and S. Horvath , “Defining clusters from a hierarchical cluster tree: The dynamic tree cut package for R,” Bioinform. Appl. Note , Vol. 24, no. 5, pp. 719–20, Dec. 2008.
  • F. Liang and N. Wang , “Dynamic agglomerative clustering of gene expression profiles,” Pattern Recognit. Lett. , Vol. 28, no. 9, pp. 1062–76, Jul. 2007.
  • Z. S. Qin , “Clustering microarray gene expression data using weighted Chinese restaurant process,” Bioinformatics , Vol. 22, pp. 1988–97, Aug. 2006.
  • S. Bandyopadhyay , A. Mukhopadhyay , and U. Maulik , “An improved algorithm for clustering gene expression data,” Bioinformatics , Vol. 23, no. 21, pp. 2859–65, Aug. 2007.
  • C.-G. Xu , K. H. Liu , and D. S. Huang , “The analysis of microarray datasets using a genetic programming,” IEEE Symp. Comput. Intell. Bioinform. Comput. Biol ., pp. 176–81, 2009.
  • J. A. Hageman , R. A. V. D. Berg , J. A. Westerhuis , M. J. V. Werf , and A. K. Smilde , “Genetic algorithm based two-mode clustering of metabolomics data,” Metabolomics Off. J. Metabolomic Soc. , Vol. 4, no. 2, pp. 141 –9, Jan. 2008.
  • V. Kumar , J. K. Chhabra , and D. Kumar , “Grey wolf-based clustering technique,” J. Intell. Syst. , Vol. 26, no. 1, pp. 153–68, Jan. 2016.
  • D. Hush and C. Scovel , “Polynomial-time decomposition algorithms for support vector machines,” Mach. Learn. , Vol. 51, no. 1, pp. 51–71, Apr. 2003.
  • S. Chu , J. Derisi , M. Eisen , J. Mulholland , D. Botstein , P. O. Brown , and I. Herskowitz , “The transcriptional program of sporulation in budding yeast,” Science (New York, N.Y.) , Vol. 282, pp. 699–705, Oct. 1998.
  • R. J. Cho , M. J. Campbell , E. A. Winzeler , L. Steinmetz , A. Conway , L. Wodica , T. G. Wolfsberg , et al. , “A genome-wide transcriptional analysis of mitotic cell cycle,” Mol. Cell , Vol. 2, pp. 65–73, Jul. 1998.
  • V. R. Iyer , M. B. Eisen , D. T. Ross , G. Schuler , T. Moore , J. C. Lee , J. M. Trent , L. M. Staudt , J. J. Hudson , M. S. Boguski , D. Lashkari , D. Shalon , D. Botstein , and P.O. Brown , “The transcriptional program in the response of human fibroblasts to serum,” Science (New York, N.Y.) , Vol. 283, pp. 83–7, 1999.
  • X. Wen , S. Fuhrman , G. S. Michaels , D. B. Carr , S. Smith , J. L. Barker , and R. Somogyi , “Large-scale temporal gene expression mapping of central nervous system development,” Proc. Natl. Acad. Sci. , Vol. 95, pp. 334–9, Jan. 1998.
  • P. Reymonda , H. Webera , M. Damonda , and E. E. Farmera , “Differential gene expression in response to mechanical wounding and insect feeding in Arabidopsis ,” Plant Cell , Vol. 12, pp. 707–20, May 2000.
  • U. Alon , N. Barkai , D. A. Notterman , K. Gishdagger , S. Ybarradagger , D. Mackdagger , and A. J. Levine , “Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays,” Proc. Natl. Acad. Sci. , Vol. 96, pp. 6745–50, Jun. 1999.
  • C. L. Blake and C. J. Merz , UCI Repository of Machine Learning , 2015. Available: https://archive.ics.uci.edu/ml/datasets/gene+expression+cancer+RNA-Seq
  • U. Maulik and S. Bandyopadhyay , “ Fuzzy partitioning using a real-coded variable-length genetic algorithm for pixel classification,” IEEE Trans. Geosci. Remote Sensing , Vol. 41, no. 5, pp. 1075–81, 2003.
  • P. J. Rousseeuw , “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” J. Comput. Appl. Math. , Vol. 20, pp. 53–65, 1987.
  • Y. Xu , V. Olman , and D. Xu , “Minimum spanning trees for gene expression data clustering,” Genome Inform. , Vol. 12, pp. 24–33, 2001.
  • M. Hollander and D. A. Wolfe , Nonparametric Statistical Methods . Hoboken, NJ : Wiley, 1999.
  • S. Saha , A. Ekbal , K. Gupta , and S. Bandyopadhyay , “Gene expression data clustering using a multiobjective symmetry based clustering technique,” Comput. Biol. Med. , Vol. 43, pp. 1965–77, Nov. 2013.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.