Pełnotekstowe zasoby PLDML oraz innych baz dziedzinowych są już dostępne w nowej Bibliotece Nauki.
Zapraszamy na


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2006 | 16 | 2 | 233-240

Tytuł artykułu

Node assignment problem in Bayesian networks

Treść / Zawartość

Warianty tytułu

Języki publikacji



This paper deals with the problem of searching for the best assignments of random variables to nodes in a Bayesian network (BN) with a given topology. Likelihood functions for the studied BNs are formulated, methods for their maximization are described and, finally, the results of a study concerning the reliability of revealing BNs' roles are reported. The results of BN node assignments can be applied to problems of the analysis of gene expression profiles.








Opis fizyczny




  • System Engineering Group, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
  • System Engineering Group, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
  • Department of Statistics, Rice University, PO Box 1892, MS138, Houston, TX 77251, USA


  • Charniak E. (1991): Bayesian networks without tears. -AI Magazine, Vol. 12, No. 4, pp. 50-63.
  • Chickering D.M. (2002): Learning equivalence classes of Bayesian-network structures. - J. Mach. Learn. Res., Vol. 2, No. 3, pp. 445-498.
  • David H.A. and Nagaraja H.N. (2003): Order Statistics. -Hoboken, New Jersey: Wiley.
  • Friedman N. (1998): The Bayesian structural EM algorithm. - Proc. 14-th Conf. Uncertainty in Artificial Intelligence, Madisin, Wisconsin, USA, pp. 129-138.
  • Friedman N. (2004): Inferring cellular networks using probabilistic graphical models. - Science, Vol. 303, No. 5659, pp. 799-805.
  • Friedman N., Linitial M., Nachman I. and Peér D. (2000): Using Bayesian networks to analyze expression data. - J. Comput. Biol., Vol. 7, Nos. 3-4, pp. 601-620.
  • Gadbury G.L. and Schreuder H.T. (2003): Cause-effect relationships in analytical surveys: An illustration of statistical issues. - Env.Monit. Assess., Vol. 83, No. 3, pp. 205-227.
  • Gilks W.R., Richardson S. and Spiegelhalter D.J. (1996): Markov Chain Monte Carlo in Practice. - London: Chapman and Hall.
  • Heckerman D. (1995): A tutorial on learning with Bayesian networks. - Tech.Rep., MSR-TR-95-06, available at:
  • Ideker T., Thorsson V., Ranish J.A., Christmas R., Buhler J., Eng J.K., Bumgarner R., Goodlett D.R., Aebersold D.R. and Hood L.(2001): Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. - Science, Vol. 292, No. 5518, pp. 929-934.
  • Ideker T., Ozier O., Schwikowski B. and Siegel A.F. (2002): Discovering regulatory and signaling circuits in molecular interaction networks. - Bioinf. Vol. 18, Suppl. 1, No. 90001, pp. S233-S240.
  • Jansen R., Yu H., Greenbaum H., Kluger Y., Krogan N.J., Chung S., Emili S.,Snyder M., Greenblatt J.F. and Gerstein M.(2003): A Bayesian networks approach for predicting protein - Protein interactions from genomic data. - Science, Vol. 302, No. 5644, pp. 449-453.
  • Jensen F.V. (2001): Bayesian Networks and Decision Graphs. -New York: Springer.
  • Murphy K. (2005): Bayes net toolbox for matlab. - Available at:
  • Liu J. and Desmarais M.C. (1997): A method of learning implication networks from empirical data: Algorithm and Monte-Carlo simulation-based validation. - IEEE Trans. Knowl. Data Eng., Vol. 9, No. 6, pp. 990-1004.
  • Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H. and Teller E.(1953): Equations of state calculations by fast computing machines. - J. Chem. Phys., Vol. 21, No. 6, pp. 1087-1092.
  • Neapolitan R.E. (2003): Learning Bayesian Networks. -Upper Saddle River, NJ: Prentice Hall.
  • Pearl J. (2000): Causality: Models, Reasoning, and Inference. -Cambridge, MA: Cambridge University Press.
  • Pearl J. and Verma T.S. (1991): A theory of inferred causation, In: Principles of Knowledge Representation and Reasoning, (J.A. Allen, R. Fikes and E. Sandewall, Eds.). - San Mateo: Morgan Kaufmann.
  • Peér D., Regev A., Elidan G. and Friedman N. (2001): Inferring subnetworks from perturbed expression profiles. - Bioinf., Vol. 17, Suppl. 1, No. 90001, pp. S215-S224.
  • Polanski A., Polanska J., Jarzab M., Wiench M. and Jarzab B.,(2005): Inferring cause - effect relations from gene expression profiles of cancer versus normal cells. - Tech. Rep., available at:
  • Rhodes D.R., Yu J., Shanker K., Deshpande N., Varambally R., Ghosh R., Barrette T., Pandey A. and Chinnaiyan A.M. (2004): ONCOMINE, A cancer microarray database and integrated data mining platform. - Neoplasia, Vol. 6, No. 1, pp. 1-6.
  • Segal E., Taskar B., Gasch A., Friedman N. and Koller D. (2001): Rich probabilistic models for gene expression. - Bioinf., Vol. 1, No. 1, pp. 1-10.

Typ dokumentu



Identyfikator YADDA

JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.