 (12) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detectionL. Van den Bulcke, A. De Backer, B. Ampe, S. Maes, J. Wittoeck, W. Waegeman, K. Hostens and S. Derycke(2021) METABARCODING AND METAGENOMICS. 5, 233-247. |
| (11) Bacterial species identification using MALDI-TOF mass spectrometry and machine learning techniques: a large-scale benchmarking study T. Mortier, A. Wieme, P. Vandamme and W. Waegeman(2021) COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL. 19, 6157-6168. |
| (10) Improving the performance of machine learning models for biotechnology: the quest for deux ex machinaF. Mey, J. Clauwaert, K. Van Huffel, W. Waegeman and M. De Mey(2021) BIOTECHNOLOGY ADVANCES. 53, 107858. |
| (9) Pressure injury prediction models for critically-ill patients should consider both the case-mix and local factors M. Deschepper, S. Labeau, W. Waegeman and S. Blot(2021) INTENSIVE AND CRITICAL CARE NURSING. 65, 103033. |
| (8) Explainability in transformer models for functional genomicsJ. Clauwaert, G. Menschaert and W. Waegeman(2021) BRIEFINGS IN BIOINFORMATICS. 22, 1-11. |
| (7) Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprintingJ. Heyse, F. Schattenberg, P. Rubbens, S. Müller, W. Waegeman, N. Boon and R. Props (2021) MSYSTEMS. 6, e00551-21. |
| (6) Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learningD. Hanssens, E. Van De Vijver, W. Waegeman, M. E. Everett, I. Moffat, A. Sarris and P. De Smedt(2021) NEAR SURFACE GEOPHYSICS . 19, 541-556. |
| (5) Efficient set-valued prediction in multi-class classificationT. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier and W. Waegeman(2021) DATA MINING AND KNOWLEDGE DISCOVERY. 35, 1435-1469. |
| (4) Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methodsE. Hüllermeier and W. Waegeman(2021) MACHINE LEARNING. 110, 457-506. |
| (3) PhenoGMM: Gaussian mixture modelling of cytometry data quantifies changes in microbial community structureP. Rubbens, R. Props, F.-M. Kerckhof, N. Boon and W. Waegeman(2021) MSPHERE. 6, e00530-20. |
| (2) High-resolution surveying with small-loop frequency domain electromagnetic systems: Efficient survey design and adaptive processingD. Hanssens, W. Waegeman, Y. Declercq, H. Dierckx, H. Verschelde and P. De Smedt(2021) IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. 9, 167-183. |
| (1) Cytometric fingerprints of gut microbiota predict Crohn's disease state P. Rubbens, R. Props, F.-M. Kerckhof, N. Boon and W. Waegeman(2021) THE ISME JOURNAL. 15, 354-358. |