KERMIT -- Research Unit Knowledge-based Systems

Journal Papers (ISI)

Year

All
2024 (1)
2023 (2)
2022 (4)
2021 (12)
2020 (5)
2019 (7)
2018 (7)
2017 (10)
2016 (2)
2015 (1)
2014 (4)
2013 (7)
2012 (6)
2011 (6)
2010 (4)
2009 (2)
2008 (4)
2007 (1)

(85) A comparison of embedding aggregation strategies in drug-target interaction prediction
D. Iliadis, B. De Baets, T. Pahikkala and W. Waegeman
(2024) BMC BIOINFORMATICS. 25, 59.

(84) DeepMTP: A Python-based deep learning framework for multi-target prediction
D.Iliadis, B. De Baets and W. Waegeman
(2023) SOFTWAREX. 23, 101516.

(83) Valid prediction intervals for regression problems
N. Dewolf, B. De Baets and W. Waegeman
(2023) ARTIFICIAL INTELLIGENCE REVIEW. 56, 577-613.

(82) Multi-target prediction for dummies using two-branch neural networks
D. Iliadis, B. De Baets and W. Waegeman
(2022) MACHINE LEARNING. 111, 651-684.

(81) CpG Transformer for imputation of single-cell methylomes
G. De Waele, J. Clauwaert, G. Menschaert and W. Waegeman
(2022) BIOINFORMATICS. 38, 597-603.

(80) Novel transformer networks for improved sequence labeling in genomics
J. Clauwaert and W. Waegeman
(2022) IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 19, 97-106.

(79) Heterogeneity hampers the identification of general pressure injury risk factors in intensive care populations: A predictive modelling analysis
M. Deschepper, S. O. Labeau, W. Waegeman and S. I. Blot
(2022) INTENSIVE AND CRITICAL CARE NURSING. 68, 103117.

(78) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection
L. 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.

(77) 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.

(76) Improving the performance of machine learning models for biotechnology: the quest for deux ex machina
F. Mey, J. Clauwaert, K. Van Huffel, W. Waegeman and M. De Mey
(2021) BIOTECHNOLOGY ADVANCES. 53, 107858.

(75) 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.

(74) Explainability in transformer models for functional genomics
J. Clauwaert, G. Menschaert and W. Waegeman
(2021) BRIEFINGS IN BIOINFORMATICS. 22, 1-11.

(73) Predicting the presence and abundance of bacterial taxa in environmental communities through flow cytometric fingerprinting
J. Heyse, F. Schattenberg, P. Rubbens, S. Müller, W. Waegeman, N. Boon and R. Props
(2021) MSYSTEMS. 6, e00551-21.

(72) Ambient temperature and relative humidity-based drift correction in frequency domain electromagnetics using machine learning
D. 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.

(71) Efficient set-valued prediction in multi-class classification
T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier and W. Waegeman
(2021) DATA MINING AND KNOWLEDGE DISCOVERY. 35, 1435-1469.

(70) Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
E. Hüllermeier and W. Waegeman
(2021) MACHINE LEARNING. 110, 457-506.

(69) PhenoGMM: Gaussian mixture modelling of cytometry data quantifies changes in microbial community structure
P. Rubbens, R. Props, F.-M. Kerckhof, N. Boon and W. Waegeman
(2021) MSPHERE. 6, e00530-20.

(68) High-resolution surveying with small-loop frequency domain electromagnetic systems: Efficient survey design and adaptive processing
D. Hanssens, W. Waegeman, Y. Declercq, H. Dierckx, H. Verschelde and P. De Smedt
(2021) IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE. 9, 167-183.

(67) 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.

(66) Predictive design of sigma factor-specific promoters
M. Van Brempt, J. Clauwaert, F. Mey, M. Stock, J. Maertens, W. Waegeman and M. De Mey
(2020) NATURE COMMUNICATIONS. 11, 5822.

(65) Using structured pathology data to predict hospital-wide mortality at admission
M. Deschepper, W. Waegeman, D. Vogelaers and K. Eeckloo
(2020) PLOS ONE. 15, e0235117.

(64) Discriminating bacterial phenotypes at the population and single-cell level: a comparison of flow cytometry and Raman spectroscopy fingerprinting
C. García-Timermans, P. Rubbens, J. Heyse, F.-M. Kerckhof, R. Props, A. G. Skirtach, W. Waegeman and N. Boon
(2020) CYTOMETRY: PART A. 97, 713-726.

(63) Fast pathogen identification using single-cell Matrix-Assisted Laser Desorption/Ionization-Aerosol Time-of-Flight mass spectrometry data and deep learning methods
C. Papagiannopoulou, R. Parchen, P. Rubbens and W. Waegeman
(2020) ANALYTICAL CHEMISTRY. 92, 7523-7531.

(62) Algebraic shortcuts for leave-one-out cross-validation in supervised network inference
M. Stock, T. Pahikkala, A. Airola, W. Waegeman and B. De Baets
(2020) BRIEFINGS IN BIOINFORMATICS. 21, 262-271.

(61) 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.

(60) 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.

(59) 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.

(58) 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.

(57) 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.

(56) 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.

(55) 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.

(54) Terrestrial evaporation response to modes of climate variability
B. Martens, W. Waegeman, W.A. Dorigo, N.E.C. Verhoest and D.G. Miralles
(2018) NPJ CLIMATE AND ATMOSPHERIC SCIENCE. 1, 43.

(53) Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data
R. Props, P. Rubbens, M. Besmer, B. Buysschaert, J. Sigrist, H. Weilenmann, W. Waegeman, N. Boon and F. Hammes
(2018) WATER RESEARCH. 145, 73-82.

(52) Global hydro-climatic biomes identified via multi-task learning
C. Papagiannopoulou, D.G. Miralles, M. Demuzere, N.E.C Verhoest and W. Waegeman
(2018) GEOSCIENTIFIC MODEL DEVELOPMENT. 11, 4139-4153.

(51) Effects of chlorhexidine gluconate oral care on hospital mortality: A hospital-wide, observational cohort study
M. Deschepper, W. Waegeman, K. Eeckloo, D. Vogelaers and S. Blot
(2018) INTENSIVE CARE MEDICINE. 44, 1017-1026.

(50) Label-free Raman characterization of bacteria calls for standardized procedures
C. García-Timermans, P. Rubbens, F.-M. Kerckhof, B. Buysschaert, D. Khalenkow, W. Waegeman, A. Skirtach and N. Boon
(2018) JOURNAL OF MICROBIOLOGICAL METHODS. 151, 69-75.

(49) A comparative study of pairwise learning methods based on Kernel Ridge Regression
M. Stock, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman
(2018) NEURAL COMPUTATION. 30, 2245-2283.

(48) Interpretation and visualisation of data in dairy herds
K. Hermans, G. Opsomer, W. Waegeman, S. Moerman, J. De Koster, M. Van Eetvelde, B. Van Ranst and M. Hostens
(2018) IN PRACTICE. 40, 195-203.

(47) Stripping flow cytometry: how many detectors do we need for bacterial identification?
P. Rubbens, R. Props, C. Garcia-Timmermans, N. Boon and W. Waegeman
(2017) CYTOMETRY PART A. 91, 1184-1191.

(46) Vegetation anomalies caused by antecedent precipitation in most of the world
C. Papagiannopoulou, D.G. Miralles, W.A. Dorigo, N.E.C. Verhoest, M. Depoorter and W. Waegeman
(2017) ENVIRONMENTAL RESEARCH LETTERS . 12, 074016.

(45) Novel approaches to assess the quality of fertility data stored in dairy herd management software
K. Hermans, W. Waegeman, G. Opsomer, B. Van Ranst, J. De Koster, M. Van Eetvelde and M. Hostens
(2017) JOURNAL OF DAIRY SCIENCE. 100, 4078-4089.

(44) Potentials and limitations of existing forecasting models for Alternaria on potatoes: challenges for model improvement
S. Landschoot, J. De Reu, K. Audenaert, P. Vanhaverbeke, G. Haesaert, B. De Baets and W. Waegeman
(2017) POTATO RESEARCH. 60, 61-76.

(43) A non-linear Granger causality framework to investigate climate-vegetation dynamics
C. Papagiannopoulou, D.G. Miralles, S. Decubber, M. Demuzere, N.E.C. Verhoest, W.A. Dorigo and W. Waegeman
(2017) GEOSCIENTIFIC MODEL DEVELOPMENT. 10, 1945-1960.

(42) miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure
G. Van Peer, A. De Paepe, M. Stock, J. Anckaert, P-J. Volders, J. Vandesompele, B. De Baets and W. Waegeman
(2017) NUCLEIC ACIDS RESEARCH. 45, e51.

(41) Linear filtering reveals false negatives in species interaction data
M. Stock, T. Poisot, W. Waegeman and B. De Baets
(2017) SCIENTIFIC REPORTS. 7, 45908.

(40) Flow cytometric single-cell identification of populations in synthetic bacterial communities
P. Rubbens, R. Props, N. Boon and W. Waegeman
(2017) PLOS ONE. 12, e0169754.

(39) Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils
P. Nguyen, A. Haghverdi, J. de Pue, Y-D. Botula, K. Le, W. Waegeman and W. Cornelis
(2017) BIOSYSTEMS ENGINEERING. 153C, 12-27.

(38) Absolute quantification of microbial taxon abundances
R. Props, F-M. Kerckhof, P. Rubbens, J. De Vrieze, E. Hernandez Sanabria, W. Waegeman, P. Monsieurs, F. Hammes and N. Boon
(2017) THE ISME JOURNAL. 11, 584-587.

(37) Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
M. Stock, K. Dembczyński, B. De Baets and W. Waegeman
(2016) DATA MINING AND KNOWLEDGE DISCOVERY. 30, 1370-1394.

(36) Data-driven recipe completion using machine learning methods
M. De Clercq, M. Stock, B. De Baets and W. Waegeman
(2016) TRENDS IN FOOD SCIENCE & TECHNOLOGY. 49, 1-13.

(35) Prediction of subacute ruminal acidosis based on milk fatty acids: a comparison of linear discriminant analysis and support vector machine approaches for model development
E. Colman, W. Waegeman, B. De Baets and V. Fievez
(2015) COMPUTERS AND ELECTRONICS IN AGRICULTURE. 111, 179-185.

(34) A community effort to assess and improve drug sensitivity prediction algorithms
J.C. Costello, L.M. Heiser, E. Georgii, M. Gönen, M.P. Menden, N.J. Wang, M. Bansal, M. Ammad-ud-din, P. Hintsanen, S.A. Khan, J.-P. Mpindi, O. Kallioniemi, A. Honkela, T. Aittokallio, K. Wennerberg, J.-P. Abbuehl, J. Allen, R.B. Altman, S. Balcome, A. Battle, A. Bender, B. Berger, J. Bernard, M. Bhattacharjee, K. Bhuvaneshwar, A.A. Bieberich, F. Boehm, A. Califano, C. Chan, B. Chen, T.-H. Chen, J. Choi, L.P. Coelho, T. Cokelaer, C.J. Creighton, J. Cui, W. Dampier, V.J. Davisson, B. De Baets, R. Deshpande, B. DiCamillo, M. Dundar, Z. Duren, A. Ertel, H. Fan, H. Fang, R. Gauba, A. Gottlieb, M. Grau, Y. Gusev, M.J. Ha, L. Han, M. Harris, N. Henderson, H.A. Hejase, K. Homicsko, J.P. Hou, W. Hwang, A.P. Ijzerman, B. Karacali, S. Keles, C. Kendziorski, J. Kim, M. Kim, Y. Kim, D.A. Knowles, D. Koller, J. Lee, J.K. Lee, E.B. Lenselink, B. Li, B. Li, J. Li, H. Liang, J. Ma, S. Madhavan, S. Mooney, C.L. Myers, M.A Newton, J.P. Overington, R. Pal, J. Peng, R. Pestell, R.J. Prill, P. Qiu, B. Rajwa, A. Sadanandam, F. Sambo, H. Shin, J. Song, L. Song, A. Sridhar, M. Stock, W. Sun, T. Ta, M. Tadesse, M. Tan, H. Tang, D. Theodorescu, G.M. Toffolo, A. Tozeren, W. Trepicchio, N. Varoquaux, J.-P. Vert, W. Waegeman, T. Walter, Q. Wan, D. Wang, W. Wang, Y. Wang, Z. Wang, J.K. Wegner, T. Wu, T. Xia, G. Xiao, Y. Xie, Y. Xu, J. Yang, Y. Yuan, S. Zhang, X.-S. Zhang, J. Zhao, C. Zuo, H.W.T. van Vlijmen, G.J.P. van Westen, J.J. Collins, D. Gallahan, D. Singer, J. Saez-Rodriguez, S. Kaski, J.W. Gray and G. Stolovitzky
(2014) NATURE BIOTECHNOLOGY. 32, 1202-1212.

(33) On the Bayes-optimality of F-measure maximizers
W. Waegeman, K. Dembczynski, A. Jachnik, W. Cheng and E. Hüllermeier
(2014) JOURNAL OF MACHINE LEARNING RESEARCH. 15, 3333-3388.

(32) Identification of functionally-related enzymes by learning-to-rank methods and cavity-based similarity measures
M. Stock, T. Fober, E. Hüllermeier, S. Glinca, G. Klebe, T. Pahikkala, A. Airola, B. De Baets and W. Waegeman
(2014) IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. 11, 1157-1169.

(31) Predicting spatio-temporal Culicoides imicola distributions based on environmental habitat characteristics and species dispersal
J. Peters, W. Waegeman, J. Van doninck, E. Ducheyne, C. Calvete, J. Lucientes, N.E.C. Verhoest and B. De Baets
(2014) ECOLOGICAL INFORMATICS. 22, 69-80.

(30) Exploration and prediction of interactions between methanotrophs and heterotrophs
M. Stock, S. Hoefman, F.-M. Kerckhof, N. Boon, P. De Vos, B. De Baets, K. Heylen and W. Waegeman
(2013) RESEARCH IN MICROBIOLOGY. 10, 1045-1054.

(29) Ordinal regression models for predicting deoxynivalenol in winter wheat
S. Landschoot, W. Waegeman, K. Audenaert, G. Haesaert and B. De Baets
(2013) PLANT PATHOLOGY. 62, 1319-1329.

(28) Efficient regularized least-squares algorithms for conditional ranking on relational data
T. Pahikkala, A. Airola, M. Stock, B. De Baets and W. Waegeman
(2013) MACHINE LEARNING. 93, 321-356.

(27) Combined exposure of cyanobacteria and carbaryl results in antagonistic effects on the reproduction of Daphnia pulex
J. Asselman, J. Meys, W. Waegeman, B. De Baets and K.A.C. De Schamphelaere
(2013) ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. 32, 2153-2158.

(26) Influence of maize-wheat rotation systems on Fusarium head blight infection and deoxynivalenol content in wheat under low versus high disease pressure
S. Landschoot, K. Audenaert, W. Waegeman, B. De Baets and G. Haesaert
(2013) CROP PROTECTION. 52, 14-21.

(25) Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models
S. Fukuda, B. De Baets, W. Waegeman, J. Verwaeren and A.M. Mouton
(2013) ENVIRONMENTAL MODELLING AND SOFTWARE. 447, 1-6.

(24) A field-specific web tool for the prediction of Fusarium head blight and DON content in Belgium
S. Landschoot, W. Waegeman, K. Audenaert, P. Van Damme, J. Vandepitte, B. De Baets and G. Haesaert
(2013) COMPUTERS AND ELECTRONICS IN AGRICULTURE. 93, 140-148.

(23) A kernel-based framework for learning graded relations from data
W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock and B. De Baets
(2012) IEEE TRANSACTIONS ON FUZZY SYSTEMS. 20, 1090-1101.

(22) The logistic curve as a tool to describe the daily ruminal pH pattern and its link with milk fatty acids
E. Colman, B. Thas, W. Waegeman, B. De Baets and V. Fievez
(2012) J. DAIRY SCIENCE. 95, 5845-5865.

(21) On label dependence and loss minimization in multi-label classification
K. Dembczynski, W. Waegeman, W. Cheng and E. Hüllermeier
(2012) MACHINE LEARNING. 88, 5-45.

(20) Towards a reliable evaluation of forecasting systems for plant diseases: A case study of Fusarium head blight
S. Landschoot, W. Waegeman, K. Audenaert, J. Vandepitte, G. Haesaert and B. De Baets
(2012) PLANT DISEASE. 96, 889-896.

(19) An empirical analysis of explanatory variables affecting Fusarium head blight infection and deoxynivalenol content in wheat
S. Landschoot, W. Waegeman, K. Audenaert, J. Vandepitte, J.M. Baetens, B. De Baets and G. Haesaert
(2012) JOURNAL OF PLANT PATHOLOGY. 94, 135-147.

(18) Learning partial ordinal class memberships with kernel-based proportional odds models
J. Verwaeren, W. Waegeman and B. De Baets
(2012) COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 56, 928-942.

(17) Supervised learning algorithms for multi-class classification problems with partial class memberships
W. Waegeman, J. Verwaeren, B. Slabbinck and B. De Baets
(2011) FUZZY SETS AND SYSTEMS. 184, 106-125.

(16) Connection between primary Fusarium inoculum on gramineous weeds, crop residues and soil samples and the final population on wheat ears in Flanders
S. Landschoot, K. Audenaert, W. Waegeman, B. Pycke, B. Bekaert, B. De Baets and G. Haesaert
(2011) CROP PROTECTION. 30, 1297-1305.

(15) Effect of model formulation on the optimization of a genetic Takagi-Sugeno fuzzy system for fish habitat suitability evaluation
S. Fukuda, B. De Baets, A.M. Mouton, W. Waegeman, J. Nakajima, T. Mukai, K. Hiramatsu and N. Onikura
(2011) ECOLOGICAL MODELLING. 222, 1401-1413.

(14) On the ERA representability of pairwise bipartite ranking functions
W. Waegeman and B. De Baets
(2011) ARTIFICIAL INTELLIGENCE JOURNAL. 175, 1223-1250.

(13) Bacterial species identification from MALDI-TOF spectra through data analysis and machine learning
K. De Bruyne, B. Slabbinck, W. Waegeman, P. Vauterin, B. De Baets and P. Vandamme
(2011) SYSTEMATIC AND APPLIED MICROBIOLOGY. 34, 20-29.

(12) An experimental comparison of cross-validation techniques for estimating the area under the ROC curve
A. Airola, T. Pahikkala, W. Waegeman, B. De Baets and T. Salakoski
(2011) COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 55, 1828-1844.

(11) A transitivity analysis of bipartite rankings in pairwise multi-class classification
W. Waegeman and B. De Baets
(2010) INFORMATION SCIENCES. 180, 4099-4117.

(10) On the role of cost-sensitive learning in multi-class brain-computer interfaces
D. Devlaminck, W. Waegeman, B. Wyns, G. Otte and P. Santens
(2010) BIOMEDICAL ENGINEERING. 55, 163-172.

(9) Learning intransitive reciprocal relations with kernel methods
T. Pahikkala, W. Waegeman, E. Tsivtsivadze, T. Salakoski and B. De Baets
(2010) EUROPEAN J. OPER. RES.. 206, 676-685.

(8) From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification
B. Slabbinck, W. Waegeman, P. Dawyndt, P. De Vos and B. De Baets
(2010) BMC BIOINFORMATICS. 11, 69.

(7) Kernel-based learning methods for preference aggregation
W. Waegeman, B. De Baets and L. Boullart
(2009) 4OR. 7, 169-189.

(6) Learning to Rank: a ROC-based Graph-Theoretic Approach
W. Waegeman
(2009) 4OR. 7, 399-402.

(5) Learning layered ranking functions with structured Support Vector Machines
W. Waegeman, B. De Baets and L. Boullart
(2008) NEURAL NETWORKS. 21, 1511-1523.

(4) Classifying carpets based on laser scanner data
W. Waegeman, J. Cottyn, B. Wyns, L. Boullart, B. De Baets, L. Van Langenhove and J. Detand
(2008) ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. 21, 907-918.

(3) On the scalability of ordered multi-class ROC analysis
W. Waegeman, B. De Baets and L. Boullart
(2008) COMPUTATIONAL STATISTICS AND DATA ANALYSIS. 52, 3371-3388.

(2) ROC analysis in ordinal regression learning
W. Waegeman, B. De Baets and L. Boullart
(2008) PATTERN RECOGNITION LETTERS. 29, 1-9.

(1) Polymorphisms in the Ficolin 1 gene (FCN1) are associated with susceptibility to the development of rheumatoid arthritis
B. Vander Cruyssen, L. Nuytinck, L. Boullart, D. Elewaut, W. Waegeman, M. Van Thielen, E. De Meester, K. Lebeer, R. Rossau and F. De Keyser
(2007) RHEUMATOLOGY. 46, 1792-1795.

Journal Papers (ISI)

Authors

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