We are looking into symbolic AI as wells game theoretical approaches to explain the predictions we make with our bioinformatics tools.  We currently focus on analysing rare diseases and try to identify with new predictive methods the origins of these diseases.  The Explainable part focusses on explaining and providing context for these predictions.  More details on the predictors and the use of game theory to explain the results can be found in the following publications.  The link with symolic AI methods is currrenty being developed. 

- Gazzo,A.M., Daneels,D., Cilia,E., Bonduelle,M., Abramowicz,M., Van Dooren,S., Smits,G. and Lenaerts,T. (2015) DIDA: A curated and annotated digenic diseases database. Nucl. Acids Res., 10.1093/nar/gkv1068.

- Gazzo A., Raimondi D., Daneels D., Moreau Y., Smits G., Van Dooren S., Lenaerts T. Understanding mutational effects in digenic diseases. Nucleic Acids Research 45(15):e140 (2017), DOI: https://doi.org/10.1093/nar/gkx557

- Versbraegen N., Fouché A., Nachtegael C., Papadimitriou S., Gazzo A., Smits G., Lenaerts T.(2019) Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases. To appear in Artificial Intelligence in Medicine.

- Papadimitriou S., Gazzo A., Versbraegen N., Nachtegael C., Aerts J., Moreau Y., Van Dooren S., Nowé A., Smits G., Lenaerts T. (2019) Predicting disease-causing variant combinations. Proceedings of the National Academy of Sciences. 116 (24), 11878-11887.

- Renaux A., Papadimitriou S., Versbraegen N., Nachtegael C., Boutry S., Nowé A., Smits G., Lenaerts T. (2019) ORVAL: A novel platform for the prediction and exploration of disease-causing oligogenic variant combinations. Nucleic Acids Research, gkz437. DOI: https://doi.org/10.1093/nar/gkz437