Skip to main content

Part of the book series: Massive Computing ((MACO,volume 2))

  • 441 Accesses

Abstract

This chapter describes opportunities for data mining in the emerging arena of bioinformatics applications. We outline the nature of research issues in bioinformatics and the motivating data management and analysis tasks. Descriptions of successful applications are given, along with an outline of the near-future potential and issues affecting the successful application of data mining.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. R.G. Alscher, L.S. Heath, B.I. Chevone, and N. Ramakrishnan. Expresso — A PSE for Bioinformatics: Finding Answers with Microarray Technology. In A. Tentner, editor, Proc. of the High Performance Computing Symposium, Advanced Simulation Technologies Conference, pages 64–69, Seattle, WA, April 2001.

    Google Scholar 

  2. D. Barbara, W. DuMouchel, C. Faloutsos, P. Haas, J. Hellerstein, Y. Ioannidis, H. Jagadish, T. Johnson, R. Ng, V. Poosala, K. Ross, and K. Sevcik. The New Jersey Data Reduction Report. Bulletin of the IEEE Technical Committee on Data Engineering, Vol. 20(4):pp. 3–45, December 1997.

    Google Scholar 

  3. P. Buneman, S. Davidson, K. Hart, C. Overton, and L. Wong. A Data Transformation System for Biological Data Sources. Proc. of the VLDB Conference, 1995.

    Google Scholar 

  4. M.P.S. Brown, W.N. Grundy, D. Lin, N. Cristianini, C.W. Sugnet, T.S. Purey, M. Ares Jr., and D. Haussler. Knowledge-Based Analysis of Microarray Gene Expression Data by Using Support Vector Machines. Proceedings of the National Academy of Science, Vol. 97(l):pp. 262–267, January 2000.

    Article  Google Scholar 

  5. K.M. Chandy, R. Bramley, B.W. Char, and J.V.W. Reynders. Report of the NSF Workshop on Problem Solving Environments and Scientific IDEs for Knowledge, Information and Computing (SIDEKIC’98). Technical report, Los Alamos National Laboratory, 1998.

    Google Scholar 

  6. M. Craven and J. Shavlik. Learning to Represent Codons: A Challenge Problem for Constructive Induction. Proc. of the Thirteenth International Joint Conference on Artificial Intelligence, pages 1319–1324, 1993. Chambery, France. VNIT

    Google Scholar 

  7. S. Chaudhuri and K. Shim. Optimization of Queries with User-Defined Predicates. ACM Transactions on Database Systems, Vol. 24(2):pp. 177–228, June 1999.

    Article  Google Scholar 

  8. D.J. Duggan, M. Bittner, Y. Chen, P. Meltzer, and J.M. Trent. Expression Profiling Using cDNA Microarrays. Nature Genetics, Vol. 21:pp. 10–14, 1999.

    Article  Google Scholar 

  9. T.S. Purey, N. Cristianini, N. Duffy, D.W. Bednarski, M. Schummer, and D. Haussler. Support Vector Machine Classification and Validation of Cancer Tissue Samples Using Microarray Expression Data. Bioinformatics, Vol. 16(10):pp. 906–914, 2000.

    Article  Google Scholar 

  10. D. Fensel, N. Kushmerick, C. Knoblock, and M.-C. Rousset. Proceedings of the IJCAI-99 Workshop on Intelligent Information Integration. International Joint Conference on Artificial Intelligence, Stockholm, 1999.

    Google Scholar 

  11. H. Garcia-Molina, J.D. Ullman, and J. Widom. Database System Implementation. Prentice Hall, 2000.

    Google Scholar 

  12. H. Hamadeh and C.A. Afshari. Gene Chips and Functional Genomics. American Scientist, Vol. 88:pp. 508–515, November-December 2000.

    Article  Google Scholar 

  13. D. Heckerman. Bayesian Networks for Knowledge Discovery. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 273–306. AAAI/MIT Press, 1996.

    Google Scholar 

  14. J.M. Hellerstein. Optimization Techniques for Queries with Expensive Methods. A CM Transactions on Database Systems, Vol. 23(2):pp. 113–157, September 1998.

    Article  MathSciNet  Google Scholar 

  15. Scanalytics Inc. Scanalytics Microarray Suite. http://www.scanalytics.com, 2000.

  16. Kazusa DNA Research Institute. CyanoBase. URL: http://www.kazusa.or.jp/cyanobase/, 1996.

    Google Scholar 

  17. M.-L. T. Lee, F.C. Kuo, G.A. Whitmore, and J. Sklar. Importance of Replication in Microarray Gene Expression Studies: Statistical Methods and Evidence from Repetitive cDNA Hybridizations. Proceedings of the National Academy of Science, Vol. 97(18):pp. 9834–9839, August 2000.

    Article  MATH  Google Scholar 

  18. R.W. Moore, C. Baru, R. Marciano, A. Rajasekar, and M. Wan. Data-Intensive Computing. In C. Kesselman and I. Foster, editors, The Grid: Blueprint for a New Computing Infrastructure, chapter 5, pages 107–129. Morgan Kaufmann, 1998.

    Google Scholar 

  19. R.W. Moore, T.A. Prince, and M. Ellisman. Data-Intensive Computing and Digital Libraries. Communications of the ACM, Vol. 41(11):pp. 56–62, November 1998.

    Article  Google Scholar 

  20. S. Muggleton. Scientific Knowledge Discovery Using Inductive Logic Programming. Communications of the ACM, Vol. 42(11):pp. 42–46, 1999.

    Article  Google Scholar 

  21. J.R. Rice and R.F. Boisvert. From Scientific Software Libraries to Problem-Solving Environments. IEEE Computational Science & Engineering, Vol. 3(3):pp. 44–53, Fall 1996.

    Article  Google Scholar 

  22. M. Ridley. Genome: The Autobiography of a Species in 23 Chapters. HarperCollins, 2000.

    Google Scholar 

  23. S.L. Salzberg. Decision Trees and Markov Chains for Gene Finding. In S. L. Salzberg, D. B. Searls, and S. Kasif, editors, Computational Methods in Molecular Biology, pages 187–203. Elsevier, 1998.

    Chapter  Google Scholar 

  24. S. Schulze-Kremer. Disccovery in the Human Genome Project. Communications of the ACM, Vol. 42(11):pp. 62–64, November 1999.

    Article  Google Scholar 

  25. V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.

    Book  MATH  Google Scholar 

  26. M.R. Wilkins, K.L. Williams, R.D. Appel, and D.F. Hochstrasser (Eds.). Proteome Research: New Frontiers in Functional Genomics (Principles and Practice). Springer Verlag, 1997.

    Google Scholar 

  27. D.P. Yee and D. Conklin. Automated Clustering and Assembly of Large EST Collections. Proc. of the International Conference on Intelligent Systems for Molecular Biology (ISMB), 1998.

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Ramakrishnan, N., Grama, A.Y. (2001). Data Mining Applications in Bioinformatics. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds) Data Mining for Scientific and Engineering Applications. Massive Computing, vol 2. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1733-7_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4615-1733-7_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4020-0114-7

  • Online ISBN: 978-1-4615-1733-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics