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2006 | 16 | 2 | 233-240
Tytuł artykułu

Node assignment problem in Bayesian networks

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
16
Numer
2
Strony
233-240
Opis fizyczny
Daty
wydano
2006
otrzymano
2005-09-20
poprawiono
2006-03-14
Twórcy
  • System Engineering Group, Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
autor
  • 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
Bibliografia
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  • 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:ftp://ftp.research.microsoft.com/pub/tr/tr-95-06.pdf
  • 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: http://bnt.sourceforge.net/
  • 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: http://web.zis.ia.polsl.gliwice.pl/publikacje/projekty/technical_report.pdf
  • 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
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.bwnjournal-article-amcv16i2p233bwm
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