Journal Papers (ISI)

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