 (7) Randomized lasso links microbial taxa with aquatic functional groups inferred from flow cytometryP. Rubbens, M. Schmidt, R. Props, B. Biddanda, N. Boon, W. Waegeman and V. Denef(2019) MSYSTEMS. 4, 00093-19. |
| (6) A protocol for automated timber species identification using metabolome profilingV. Deklerck, T. Mortier, N. Goeders, R.B. Cody, W. Waegeman, E. Espinoza, J. Van Acker, J. Van den Bulcke and H. Beeckman(2019) WOOD SCIENCE AND TECHNOLOGY. 53, 953-965. |
| (5) A hospital wide predictive model for unplanned readmission using hierarchical ICD dataM. Deschepper, K. Eeckloo, D. Vogelaers and W. Waegeman(2019) COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE. 173, 177-183. |
| (4) Learning single-cell distances from cytometry dataB. Nguyen, P. Rubbens, F.-M. Kerckhof, N. Boon, B. De Baets and W. Waegeman(2019) CYTOMETRY PART A. 95, 782-791. |
| (3) Coculturing bacteria leads to reduced phenotypic heterogeneitiesJ. Heyse, B. Buysschaert, R. Props, P. Rubbens, A. Skirtach, W. Waegeman and N. Boon(2019) APPLIED AND ENVIRONMENTAL MICROBIOLOGY. 85, e02814-18. |
| (2) DeepRibo: a neural network for the precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patternsJ. Clauwaert, G. Menschaert and W. Waegeman(2019) NUCLEIC ACIDS RESEARCH. 47, e36. |
| (1) Multi-target prediction: a unifying view on problems and methodsW. Waegeman, K. Dembczyński and E. Hüllermeier(2019) DATA MINING AND KNOWLEDGE DISCOVERY. 33, 293-324. |