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Learning Semi-Markovian Causal Models using Experiments.

S. Meganck, S. Maes, P. Leray, and B. Manderick. Probabilistic Graphical Models, page 195-206. (2006)

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Conservative independence-based causal structure learning in absence of adjacency faithfulness.J. Lemeire, S. Meganck, F. Cartella, and T. Liu. Int. J. Approx. Reasoning, 53 (9): 1305-1325 (2012)inSilicoDb: an R/Bioconductor package for accessing human Affymetrix expert-curated datasets from GEO.J. Taminau, D. Steenhoff, A. Coletta, S. Meganck, C. Lazar, V. de Schaetzen, R. Duque, C. Molter, H. Bersini, A. Nowé and 1 other author(s). Bioinformatics, 27 (22): 3204-3205 (2011)Batch effect removal methods for microarray gene expression data integration: a survey.C. Lazar, S. Meganck, J. Taminau, D. Steenhoff, A. Coletta, C. Molter, D. Solís, R. Duque, H. Bersini, and A. Nowé. Briefings in Bioinformatics, 14 (4): 469-490 (2013)Validation of Merging Techniques for Cancer Microarray Data Sets.J. Taminau, S. Meganck, D. Solís, W. van Staveren, G. Dom, D. Venet, H. Bersini, V. Detours, and A. Nowé. Austr. J. Intelligent Information Processing Systems, (2009)Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach.S. Meganck, P. Leray, and B. Manderick. MDAI, volume 3885 of Lecture Notes in Computer Science, page 58-69. Springer, (2006)Identification in Chain Multi-Agent Causal Models.S. Maes, S. Meganck, and B. Manderick. FLAIRS Conference, page 791-792. AAAI Press, (2005)Distributed learning of Multi-Agent Causal Models.S. Meganck, S. Maes, B. Manderick, and P. Leray. IAT, page 285-288. IEEE Computer Society, (2005)A Learning Algorithm for Multi-Agent Causal Models.S. Meganck, S. Maes, B. Manderick, and P. Leray. EUMAS, page 190-201. Koninklijke Vlaamse Academie van Belie voor Wetenschappen en Kunsten, (2005)GENESHIFT: A Nonparametric Approach for Integrating Microarray Gene Expression Data Based on the Inner Product as a Distance Measure between the Distributions of Genes.C. Lazar, J. Taminau, S. Meganck, D. Steenhoff, A. Coletta, D. Solís, C. Molter, R. Duque, H. Bersini, and A. Nowé. IEEE/ACM Trans. Comput. Biology Bioinform., 10 (2): 383-392 (2013)A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis.C. Lazar, J. Taminau, S. Meganck, D. Steenhoff, A. Coletta, C. Molter, V. de Schaetzen, R. Duque, H. Bersini, and A. Nowé. IEEE/ACM Trans. Comput. Biology Bioinform., 9 (4): 1106-1119 (2012)

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Learning Semi-Markovian Causal Models using Experiments.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning Semi-Markovian Causal Models using Experiments.

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Learning Semi-Markovian Causal Models using Experiments.