Dr. Stijn Meganck

I am one of the coordinators of the BN@work society, which groups researchers in the field of Probabilistic Graphical Models.

I'm currently working on the In Silico Wet Lab Research project initiated in the framework of the Brussels Impulse Programme for ICT, which is supported by ISRIB, the Institute for the encouragement of Scientific Research and Innovation of Brussels, and by the Brussels Capital Region.

I am also currently working on the Prognostics for Optimal Maintenance Research project, an SBO project supported by IWT as a member of the ETRO lab.

I got my Ph.D. working at the CoMo lab on a 4-year research grant awarded by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen) IWT.

I am currently working on bio-informatics/systems biology. Main points of interest are: data-merging and data-modeling.

The research for my dissertation was located in the field of Probabilistic Graphical Models of Causal Information.

My dissertation iss entitled: Towards an Integral Approach for Modeling Causality.

Research topics: Causality, Graphical Models, Probability theory, Bio-informatics

* Content-based large scale microarray analysis to identify generic stable genes using superstatistics

Frequently used reference genes

List of studies in analysis

* Leray, P., Meganck, S., Maes, S., Manderick, B., Causal Graphical Models with Latent Variables: Learning and Inference. Innovations in Bayesian Networks (Editor: Dawn E Holmes).

* Meganck, S., Leray, P., Manderick, B.,Causal Discovery in Non-Ideal Frameworks, Information - Interaction - Intelligence, Vol. 9, n.1, 2009.

* Maes, S., Meganck, S., Manderick, B., Inference in Multi-Agent Causal Models. International Journal of Approximate Reasoning, IJAR, Pages 274-299, October 2007.

* Meganck, S., Leray, P., Manderick, B., Experiment Strategy for Causal Discovery, ECSQARU 2007.

* Maes, S., Meganck, S., Philippe Leray, An integral approach to causal inference with latent variables. Causality and Probability in the Sciences, Texts In Philosophy series, January 2007.

* Meganck, S., Leray, P., Manderick, B., Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach. Modelling Decisions in Artificial Intelligence, MDAI 2006, LNCS 3885, Tarragona, April 2006.

* Lemeire, J., Meganck, S., Cartella, F., Robust Independence-Based Causal Structure Learning in Absence of Adjacency Faithfulness, PGM 2010.

* Taminau, J., Hillewaere, R., Meganck, S., Conklin, D., Nowe, A., Manderick, B., Applying Subgroup Discovery for the Analysis of String Quartet Movements, Machine Learning and Music Workshop at ACM 2010.

* Taminau, J., Hillewaere, R., Meganck, S., Conklin, D., Nowe, A., Manderick, B., Descriptive Mining of Folk Music: A Testcase, The 21st Benelux Conference on Artificial Intelligence, 2009.

* Taminau, J., Hillewaere, R., Meganck, S., Conklin, D., Nowe, A., Manderick, B., Descriptive Subgroup Mining of Folk Music, Machine Learning and Music Workshop at ECML-PKDD, September 2009.

* Meganck, S., Leray, P., Manderick, B., UnCaDo: Unsure Causal Discovery, Journées Francophone sur les Réseaux Bayésiens, May 2008.

* Ingkasuwan, P., Meganck, S., Cheevadhanarak S., Netrphan S., Meechai A., Prasitwattanaseree S., Chaijaruwanich J, Tanticharoen M., Bhumiratana S., Inferring Genes Regulation Network of Starch metabolism of Arabidopsis thaliana using Graphical Gaussian Model, Second FEBS Advanced Lecture Course on Systems Biology, FEBS-SysBio2007.

* Maes, S., Meganck, S., Leray, P., Manderick, B., Experimental Learning of Causal Models with Latent Variables, NIPS 2006 Workshop on Causality and Feature Selection.

* Meganck, S., Maes, S., Leray, P., Manderick, B., Learning Semi-Markovian Causal Models using Experiments. The third European Workshop on Probabilistic Graphical Models, PGM 06.

* Meganck, S., Leray, P., Manderick, B., Learning Causal Bayesian Networks from Observations and Experiments: A Decision Theoretic Approach. Modelling Decisions in Artificial Intelligence, MDAI 2006, LNCS 3885, Tarragona, April 2006.

* Meganck, S., Leray, P., Maes, S., Manderick, B. Apprentissage des reseaux Bayesiens causaux a partir de donnees d'observation et d'experimentation. 15e congres francophone AFRIF-AFIA Reconnaissance des Formes et Intelligence Artificielle,RFIA 2006, Tours, January 2006.

* Meganck, S., Maes, S., Leray, P., Manderick, B., A Learning Algorithm for Multi-Agent Causal Models. The Third European Workshop on Multi-Agent Systems, EUMAS2005, Brussels, December 2005.

* Maes, S., Meganck, S., Manderick, B., Causal Inference in Multi-Agent Causal Models. The 17th Belgian-Dutch Conference on Artificial Intelligence, BNAIC '05, Brussels, October 2005.

* Meganck, S., Maes, S., Leray, P., Manderick, B., Distributed Learning of Multi-Agent Causal Models. The 2005 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT'05), Compiegne, September 2005.

* Maes, S., Meganck, S., Manderick, B., Identification in Chain Multi-Agent Causal Models. Proceedings of the Special Track on Uncertain Reasoning at the 18th International FLAIRS Conference, FLAIRS 2005, Clearwater Beach, May 2005.

* Maes, S., Meganck, S., Manderick, B., Identification of Causal Effects in Multi-Agent Causal Models. Proceedings of the International Conference on Artificial Intelligence and Applications, AIA 2005, Innsbruck, February 2005.

* Maes, S., Meganck, S., Manderick, B., Causal Inference in Multi-Agent Causal Models. Proceedings of Modeles graphiques probabilistes pour la modelisation des connaissances: inference, apprentissage et applications, EGC05, Paris, January 2005.

* Maes, S., Meganck, S., Manderick, B. Multi-Agent Identification of Causal Effects. Proceedings of the Second European Workshop on Multi-Agent Systems, p409-417, EUMAS2004, Barcelona, December 2004.