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@article{IJAMCS_2006_16_2_a7, author = {Pola\'nska, J. and Borys, D. and Pola\'nski, A.}, title = {Node assignment problem in {Bayesian} networks}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {233--240}, publisher = {mathdoc}, volume = {16}, number = {2}, year = {2006}, language = {en}, url = {http://geodesic.mathdoc.fr/item/IJAMCS_2006_16_2_a7/} }
TY - JOUR AU - Polańska, J. AU - Borys, D. AU - Polański, A. TI - Node assignment problem in Bayesian networks JO - International Journal of Applied Mathematics and Computer Science PY - 2006 SP - 233 EP - 240 VL - 16 IS - 2 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IJAMCS_2006_16_2_a7/ LA - en ID - IJAMCS_2006_16_2_a7 ER -
%0 Journal Article %A Polańska, J. %A Borys, D. %A Polański, A. %T Node assignment problem in Bayesian networks %J International Journal of Applied Mathematics and Computer Science %D 2006 %P 233-240 %V 16 %N 2 %I mathdoc %U http://geodesic.mathdoc.fr/item/IJAMCS_2006_16_2_a7/ %G en %F IJAMCS_2006_16_2_a7
Polańska, J.; Borys, D.; Polański, A. Node assignment problem in Bayesian networks. International Journal of Applied Mathematics and Computer Science, Tome 16 (2006) no. 2, pp. 233-240. http://geodesic.mathdoc.fr/item/IJAMCS_2006_16_2_a7/
[1] Charniak E. (1991): Bayesian networks without tears. - AI Magazine, Vol. 12, No. 4, pp. 50-63.
[2] Chickering D.M. (2002): Learning equivalence classes of Bayesian-network structures. - J. Mach. Learn. Res., Vol. 2, No. 3, pp. 445-498.
[3] David H.A. and Nagaraja H.N. (2003): Order Statistics. - Hoboken, New Jersey: Wiley.
[4] Friedman N. (1998): The Bayesian structural EM algorithm. - Proc. 14-th Conf. Uncertainty in Artificial Intelligence, Madisin, Wisconsin, USA, pp. 129-138.
[5] Friedman N. (2004): Inferring cellular networks using probabilistic graphical models. - Science, Vol. 303, No. 5659, pp. 799-805.
[6] Friedman N., Linial M., Nachman I. and Pe'er D. (2000): Using Bayesian networks to analyze expression data. - J. Comput. Biol., Vol. 7, Nos. 3-4, pp. 601-620.
[7] 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.
[8] Gilks W.R., Richardson S. and Spiegelhalter D.J. (1996): Markov Chain Monte Carlo in Practice.-London: Chapman and Hall.
[9] 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
[10] 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.
[11] Ideker T., Ozier O., Schwikowski B. and Siegel A.F. (2002): Discovering regulatory and signaling circuits in molecularinteraction networks. - Bioinf. Vol. 18, Suppl. 1, No. 90001, pp. S233-S240.
[12] 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.
[13] Jensen F.V. (2001): Bayesian Networks and Decision Graphs. - New York: Springer.
[14] Murphy K. (2005): Bayes net toolbox for matlab. - Available at: http://bnt.sourceforge.net/
[15] 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.
[16] 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.
[17] Neapolitan R.E. (2003): Learning Bayesian Networks.-Upper Saddle River, NJ: Prentice Hall.
[18] Pearl J. (2000): Causality: Models, Reasoning, and Inference. -Cambridge, MA: Cambridge University Press.
[19] 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.
[20] Pe'er 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.
[21] 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
[22] 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.
[23] 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.