Advertisement

Bayesian Networks Learning for Gene Expression Datasets

  • Giacomo Gamberoni
  • Evelina Lamma
  • Fabrizio Riguzzi
  • Sergio Storari
  • Stefano Volinia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3646)

Abstract

DNA arrays yield a global view of gene expression and can be used to build genetic networks models, in order to study relations between genes. Literature proposes Bayesian network as an appropriate tool for develop similar models. In this paper, we exploit the contribute of two Bayesian network learning algorithms to generate genetic networks from microarray datasets of experiments performed on Acute Myeloid Leukemia (AML).

In the results, we present an analysis protocol used to synthesize knowledge about the most interesting gene interactions and compare the networks learned by the two algorithms. We also evaluated relations found in these models with the ones found by biological studies performed on AML.

Keywords

Acute Myeloid Leukemia Bayesian Network Association Rule Gene Expression Dataset Causal Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)Google Scholar
  2. 2.
    Alizadeh, A.A., Eisen, M.B., Davis, R.E., Ma, C., Lossos, I.S., Rosenwald, A., Boldrick, J.C., Sabet, H., Tran, T., Yu, X., Powell, J.I., Yang, L., Marti, G.E., Moore, T., Hudson Jr., J., Lu, L., Lewis, D.B., Tibshirani, R., Sherlock, G., Chan, W.C., Greiner, T.C., Weisenburger, D.D., Armitage, J.O., Warnke, R., Levy, R., Wilson, W., Grever, M.R., Byrd, J.C., Botstein, D., Brown, P.O., Staudt, L.M.: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000)CrossRefGoogle Scholar
  3. 3.
    Anderson, D.R., Sweeney, D.J., Williams, T.A.: Introduction to statistics concepts and applications, 3rd edn. West Publishing Company, St. Paul (1994)Google Scholar
  4. 4.
    Berry, J.A., Linoff, G.S.: Data Mining Techniques for Marketing, Sales and Customer Support. John Wiley & Sons, Chichester (1997)Google Scholar
  5. 5.
    Cheng, J., Greiner, R., Kelly, J., Bell, D., Liu, W.: Learning Bayesian networks from data: An information-theory based approach. Artificial Intelligence 137, 43–90 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Cooper, G., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347 (1992)zbMATHGoogle Scholar
  7. 7.
    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, 14863–14688 (1998)Google Scholar
  8. 8.
    Faderl, S., Kantarjian, H.M., Estey, E., Manshouri, T., Chan, C.Y., Rahman Elsaied, A., Kornblau, S.M., Cortes, J., Thomas, D.A., Pierce, S., Keating, M.J., Estrov, Z., Albitar, M.: The prognostic significance of p16(INK4a)/p14(ARF) locus deletion and MDM-2 protein expression in adult acute myelogenous leukemia. Cancer 89(9), 1976–1982 (2000)CrossRefGoogle Scholar
  9. 9.
    Friedman, N., Linial, M., Nachman, I., Pe’er, D.: Using Bayesian Networks to Analyze Expression Data. Journal of Computational Biology 3/4(7), 601–620 (2000)CrossRefGoogle Scholar
  10. 10.
    Friedman, N.: Inferring Cellular Networks Using Probabilistic Graphical Models. Science 303, 799–805 (2004)CrossRefGoogle Scholar
  11. 11.
    Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., Lander, E.S.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)CrossRefGoogle Scholar
  12. 12.
    Heckerman, D., Geiger, D., Chickering, D.: Learning Bayesian Networks: the combination of knowlegde and statistical data. Machine Learning 20, 197–243 (1995)zbMATHGoogle Scholar
  13. 13.
    Lamma, E., Riguzzi, F., Storari, S.: Exploiting Association and Correlation Rules Parameters for Improving the K2 Algorithm. In: 16th European Conference on Artificial Intelligence, pp. 500–504 (2004)Google Scholar
  14. 14.
    Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. J. Royal Statistics Society B 50, 157–194 (1988)zbMATHMathSciNetGoogle Scholar
  15. 15.
    Oshima, Y., Ueda, M., Yamashita, Y., Choi, Y.L., Ota, J., Ueno, S., Ohki, R., Koinuma, R., Wada, T., Ozawa, K., Fujimura, A., Mano, H.: DNA microarray analysis of hematopoietic stem cell-like fractions from individuals with the M2 subtype of acute myeloid leukemia. Leukemia 17(10), 1900–1997 (2003)CrossRefGoogle Scholar
  16. 16.
    Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San Francisco (1988)Google Scholar
  17. 17.
    Ramoni, M., Sebastiani, P.: Robust learning with missing data. Technical Report, Knowledge Media Institute, The Open University KMI-TR-28 (1996)Google Scholar
  18. 18.
    Stephens, P., Hunter, C., Bignell, G., Edkins, S., Davies, H., Teague, J., Stevens, C., O’Meara, S., Smith, R., Parker, A., Barthorpe, A., Blow, M., Brackenbury, L., Butler, A., Clarke, O., Cole, J., Dicks, E., Dike, A., Drozd, A., Edwards, K., Forbes, S., Foster, R., Gray, K., Greenman, C., Halliday, K., Hills, K., Kosmidou, V., Lugg, R., Menzies, A., Perry, J., Petty, R., Raine, K., Ratford, L., Shepherd, R., Small, A., Stephens, Y., Tofts, C., Varian, J., West, S., Widaa, S., Yates, A., Brasseur, F., Cooper, C.S., Flanagan, A.M., Knowles, M., Leung, S.Y., Louis, D.N., Looijenga, L.H., Malkowicz, B., Pierotti, M.A., Teh, B., Chenevix-Trench, G., Weber, B.L., Yuen, S.T., Harris, G., Goldstraw, P., Nicholson, A.G., Futreal, P.A., Wooster, R., Stratton, M.R.: Lung cancer: intragenic ERBB2 kinase mutations in tumours. Nature 431(7008), 525–526 (2004)CrossRefGoogle Scholar
  19. 19.
    Stirewalt, D.L., Radich, J.P.: Malignancy: Tumor Suppressor Gene Aberrations in Acute Myelogenous Leukemia. Hematology 5(1), 15–25 (2000)Google Scholar
  20. 20.
    Suzuki, J.: Learning Bayesian Belief Networks Based on the MDL principle: An Efficient Algorithm Using the Branch and Bound Technique. IEICE Transactions on Communications Electronics Information and Systems (1999)Google Scholar
  21. 21.
    Verstovsek, S., Kantarjian, H., Estey, E., Aguayo, A., Giles, F.J., Manshouri, T., Koller, C., Estrov, Z., Freireich, E., Keating, M., Albitar, M.: Plasma hepatocyte growth factor is a prognostic factor in patients with acute myeloid leukemia but not in patients with myelodysplastic syndrome. Leukemia 15(8), 1165–1170 (2001)CrossRefGoogle Scholar
  22. 22.
    Zhou, B.P., Liao, Y., Xia, W., Zou, Y., Spohn, B., Hung, M.C.: HER-2/neu induces p53 ubiquitination via Akt-mediated MDM2 phosphorylation. Nat Cell Biol 3(11), 973–982 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Giacomo Gamberoni
    • 1
  • Evelina Lamma
    • 1
  • Fabrizio Riguzzi
    • 1
  • Sergio Storari
    • 1
  • Stefano Volinia
    • 2
  1. 1.ENDIF-Dipartimento di IngegneriaUniversità di FerraraFerraraItaly
  2. 2.Dipartimento di BiologiaUniversità di FerraraFerraraItaly

Personalised recommendations