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Combined clustering models for the analysis of gene expression

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Abstract

Clustering has become one of the fundamental tools for analyzing gene expression and producing gene classifications. Clustering models enable finding patterns of similarity in order to understand gene function, gene regulation, cellular processes and sub-types of cells. The clustering results however have to be combined with sequence data or knowledge about gene functionality in order to make biologically meaningful conclusions. In this work, we explore a new model that integrates gene expression with sequence or text information.

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References

  1. M. B. Eisen, P. T. Spellman, P. O. Brown, and D. Botstein, Proc. Natl. Acad. Sci. USA 95, 14863 (1998).

    Article  ADS  Google Scholar 

  2. T. R. Golub et al., Science 286, 531 (1999).

  3. J. Khan et al., Nature Medicine 7, 673 (2001).

  4. D. Stekel, Microarray Bioinformatics (Cambridge Univ., Cambridge, 2003).

  5. F. H. C. Crick, Symp. Soc. Exp. Biol. XII, 139 (1958).

    Google Scholar 

  6. F H. C. Crick, Nature 227, 561 (1970).

    Article  ADS  Google Scholar 

  7. J. Ihmels et al., Nature Genetics 31, 370 (2002).

    Google Scholar 

  8. R. B. Altman and S. Raychaudhuri, Curr. Opin. Struct. Biol. 11, 340 (2001).

    Article  Google Scholar 

  9. S. Raychaudhuri, J. T. Chang, F Imam, and R. B. Altman, Nucl. Acids Res. 31, 4553 (2003).

    Article  Google Scholar 

  10. M. Angelova and C. Myers, J. Phys. Conf. Ser. 128, 012030 (2008).

    Article  ADS  Google Scholar 

  11. J. S. Verducci et al., Physiol. Genomics 25, 355 (2006).

    Article  Google Scholar 

  12. A. P. Demster, N. M. Laird, and D. B. Rubin, J. R. Stat. Soc. Ser. B 39(1), 1 (1977).

    MathSciNet  Google Scholar 

  13. C. E. Shannon, Bell Syst. Tech. J. 27, 389, 623 (1948).

    MathSciNet  Google Scholar 

  14. D. Jiang, C. Tang, and A. Zhang, IEEE Trans. Knowl. Data Eng. 16, 1370 (2004).

    Article  Google Scholar 

  15. E. Parzen, Ann. Math. Stat. 33, 1065 (1962).

    Article  MATH  MathSciNet  Google Scholar 

  16. F.Marincs, I. W. Manfield, J. A. Stead, et al., Biochem. J. 396, 227 (2006).

    Article  Google Scholar 

  17. http://blast.ncbi.nlm.nih.gov/Blast.cgi

  18. H. Shatkay, S. Edwards, and M. Boguski, IEEE Inte11. Syst. 17 (2), 45, (2002).

    Google Scholar 

  19. G. Gazdar and C. Mellish, Natural Language Processing in Prolog (Addison-Wesley, Apr. 1989).

  20. G. Aston and L. Burnard, The BNC Handbook: Exploring the British National Corpus with SARA (Edinburgh Univ., Edinburgh, 1998).

    Google Scholar 

  21. G. Leech, P. Rayson, and A. Wilson, Word Frequencies in Written and Spoken English: Based on the British National Corpus (Longman, London, 2001}); http://ucrel.lancs.ac.uk/bncfreq/

  22. P. Rayson and R. Garside, in Proc. of the ACL Workshop on Comparing Corpora 2000, Hong Kong, Oct. 2000, p. 1.

  23. T. Dunning, Computat. Linguistics 19, 61 (1993).

    Google Scholar 

  24. R. C. Moore, in Proc. of the 2004 Conf. on Empirical Methods in Natural Language Processing (EMNLP’04), Barselona, 2004, p. 333.

  25. M. P. Oakes and M. Farrow, Lit. Linguist. Computing 22, 85 (2007).

    Article  Google Scholar 

  26. G. Karypis, Technical Report No. 02-017, Univ. of Minnesota (2002); http://wwwusers. cs.umn.edu/karypis/cluto/

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Correspondence to M. Angelova.

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Angelova, M., Ellman, J. Combined clustering models for the analysis of gene expression. Phys. Atom. Nuclei 73, 242–246 (2010). https://doi.org/10.1134/S1063778810020067

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