Stijn Meganck

Jonatan Taminau, Stijn Meganck, Cosmin Lazar, David Steenhoff, Alain Coletta, Colin Molter, Robin Duque, Virginie de Schaetzen, David Y. Weiss Solís, Hugues Bersini, Ann Nowé: Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages. BMC Bioinformatics 13: 335 (2012)

Jan Lemeire, Stijn Meganck, Francesco Cartella, Tingting Liu: Conservative independence-based causal structure learning in absence of adjacency faithfulness. Int. J. Approx. Reasoning 53(9): 1305-1325 (2012)

Cosmin Lazar, Jonatan Taminau, Stijn Meganck, David Steenhoff, Alain Coletta, Colin Molter, Virginie de Schaetzen, Robin Duque, Hugues Bersini, Ann Nowé: A Survey on Filter Techniques for Feature Selection in Gene Expression Microarray Analysis. IEEE/ACM Trans. Comput. Biology Bioinform. 9(4): 1106-1119 (2012)

Anze Vavpetic, Vid Podpecan, Stijn Meganck, Nada Lavrac: Explaining Subgroups through Ontologies. PRICAI 2012: 625-636

Jonatan Taminau, David Steenhoff, Alain Coletta, Stijn Meganck, Cosmin Lazar, Virginie de Schaetzen, Robin Duque, Colin Molter, Hugues Bersini, Ann Nowé, David Y. Weiss Solís: inSilicoDb: an R/Bioconductor package for accessing human Affymetrix expert-curated datasets from GEO. Bioinformatics 27(22): 3204-3205 (2011)

Jan Lemeire, Stijn Meganck, Francesco Cartella, Tingting Liu, Alexander R. Statnikov: Inferring the causal decomposition under the presence of deterministic relations. ESANN 2011

Philippe Leray, Stijn Meganck, Sam Maes, Bernard Manderick: Causal Graphical Models with Latent Variables: Learning and Inference. Innovations in Bayesian Networks 2008: 219-249

Sam Maes, Stijn Meganck, Bernard Manderick: Inference in multi-agent causal models. Int. J. Approx. Reasoning 46(2): 274-299 (2007)

Stijn Meganck, Philippe Leray, Bernard Manderick: Causal Graphical Models with Latent Variables: Learning and Inference. ECSQARU 2007: 5-16

Stijn Meganck, Philippe Leray, Bernard Manderick: Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach. MDAI 2006: 58-69

Stijn Meganck, Sam Maes, Philippe Leray, Bernard Manderick: Learning Semi-Markovian Causal Models using Experiments. Probabilistic Graphical Models 2006: 195-206

Sam Maes, Stijn Meganck, Bernard Manderick: Identification of Causal Effects in Multi-Agent Causal Models. Artificial Intelligence and Applications 2005: 178-182

Sam Maes, Stijn Meganck, Bernard Manderick: Causal Inference in Multi-Agent Causal Models. BNAIC 2005: 367-368

Stijn Meganck, Sam Maes, Bernard Manderick, Philippe Leray: A Learning Algorithm for Multi-Agent Causal Models. EUMAS 2005: 190-201

Sam Maes, Stijn Meganck, Bernard Manderick: Identification in Chain Multi-Agent Causal Models. FLAIRS Conference 2005: 791-792

Stijn Meganck, Sam Maes, Bernard Manderick, Philippe Leray: Distributed learning of Multi-Agent Causal Models. IAT 2005: 285-288