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Items: 28

1.

Randomized Lasso Links Microbial Taxa with Aquatic Functional Groups Inferred from Flow Cytometry.

Rubbens P, Schmidt ML, Props R, Biddanda BA, Boon N, Waegeman W, Denef VJ.

mSystems. 2019 Sep 10;4(5). pii: e00093-19. doi: 10.1128/mSystems.00093-19.

2.

Learning Single-Cell Distances from Cytometry Data.

Nguyen B, Rubbens P, Kerckhof FM, Boon N, De Baets B, Waegeman W.

Cytometry A. 2019 Jul;95(7):782-791. doi: 10.1002/cyto.a.23792. Epub 2019 May 17.

PMID:
31099963
3.

Coculturing Bacteria Leads to Reduced Phenotypic Heterogeneities.

Heyse J, Buysschaert B, Props R, Rubbens P, Skirtach AG, Waegeman W, Boon N.

Appl Environ Microbiol. 2019 Apr 4;85(8). pii: e02814-18. doi: 10.1128/AEM.02814-18. Print 2019 Apr 15.

PMID:
30796063
4.

A hospital wide predictive model for unplanned readmission using hierarchical ICD data.

Deschepper M, Eeckloo K, Vogelaers D, Waegeman W.

Comput Methods Programs Biomed. 2019 May;173:177-183. doi: 10.1016/j.cmpb.2019.02.007. Epub 2019 Feb 13.

PMID:
30777619
5.

DeepRibo: a neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns.

Clauwaert J, Menschaert G, Waegeman W.

Nucleic Acids Res. 2019 Apr 8;47(6):e36. doi: 10.1093/nar/gkz061.

6.

Algebraic shortcuts for leave-one-out cross-validation in supervised network inference.

Stock M, Pahikkala T, Airola A, Waegeman W, De Baets B.

Brief Bioinform. 2018 Oct 16. doi: 10.1093/bib/bby095. [Epub ahead of print]

PMID:
30329015
7.

Detection of microbial disturbances in a drinking water microbial community through continuous acquisition and advanced analysis of flow cytometry data.

Props R, Rubbens P, Besmer M, Buysschaert B, Sigrist J, Weilenmann H, Waegeman W, Boon N, Hammes F.

Water Res. 2018 Nov 15;145:73-82. doi: 10.1016/j.watres.2018.08.013. Epub 2018 Aug 7.

PMID:
30121434
8.

Label-free Raman characterization of bacteria calls for standardized procedures.

García-Timermans C, Rubbens P, Kerckhof FM, Buysschaert B, Khalenkow D, Waegeman W, Skirtach AG, Boon N.

J Microbiol Methods. 2018 Aug;151:69-75. doi: 10.1016/j.mimet.2018.05.027. Epub 2018 Jun 14.

PMID:
29909167
9.

A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression.

Stock M, Pahikkala T, Airola A, De Baets B, Waegeman W.

Neural Comput. 2018 Aug;30(8):2245-2283. doi: 10.1162/neco_a_01096. Epub 2018 Jun 12.

PMID:
29894652
10.

Effects of chlorhexidine gluconate oral care on hospital mortality: a hospital-wide, observational cohort study.

Deschepper M, Waegeman W, Eeckloo K, Vogelaers D, Blot S.

Intensive Care Med. 2018 Jul;44(7):1017-1026. doi: 10.1007/s00134-018-5171-3. Epub 2018 May 9.

11.

Stripping flow cytometry: How many detectors do we need for bacterial identification?

Rubbens P, Props R, Garcia-Timermans C, Boon N, Waegeman W.

Cytometry A. 2017 Dec;91(12):1184-1191. doi: 10.1002/cyto.a.23284. Epub 2017 Nov 22.

12.

Linear filtering reveals false negatives in species interaction data.

Stock M, Poisot T, Waegeman W, De Baets B.

Sci Rep. 2017 Apr 6;7:45908. doi: 10.1038/srep45908.

13.

Novel approaches to assess the quality of fertility data stored in dairy herd management software.

Hermans K, Waegeman W, Opsomer G, Van Ranst B, De Koster J, Van Eetvelde M, Hostens M.

J Dairy Sci. 2017 May;100(5):4078-4089. doi: 10.3168/jds.2016-11896. Epub 2017 Mar 2.

14.

Flow Cytometric Single-Cell Identification of Populations in Synthetic Bacterial Communities.

Rubbens P, Props R, Boon N, Waegeman W.

PLoS One. 2017 Jan 25;12(1):e0169754. doi: 10.1371/journal.pone.0169754. eCollection 2017.

15.

miSTAR: miRNA target prediction through modeling quantitative and qualitative miRNA binding site information in a stacked model structure.

Van Peer G, De Paepe A, Stock M, Anckaert J, Volders PJ, Vandesompele J, De Baets B, Waegeman W.

Nucleic Acids Res. 2017 Apr 20;45(7):e51. doi: 10.1093/nar/gkw1260.

16.

Absolute quantification of microbial taxon abundances.

Props R, Kerckhof FM, Rubbens P, De Vrieze J, Hernandez Sanabria E, Waegeman W, Monsieurs P, Hammes F, Boon N.

ISME J. 2017 Feb;11(2):584-587. doi: 10.1038/ismej.2016.117. Epub 2016 Sep 9.

17.

RECIPE COMPLETION USING MACHINE LEARNING TECHNIQUES.

De Clercq M, Stock M, De Baets B, Waegeman W.

Commun Agric Appl Biol Sci. 2015;80(1):111-6.

PMID:
26630764
18.

Identification of Functionally Related Enzymes by Learning-to-Rank Methods.

Stock M, Fober T, Hüllermeier E, Glinca S, Klebe G, Pahikkala T, Airola A, De Baets B, Waegeman W.

IEEE/ACM Trans Comput Biol Bioinform. 2014 Nov-Dec;11(6):1157-69. doi: 10.1109/TCBB.2014.2338308.

PMID:
26357052
19.

Exploration and prediction of interactions between methanotrophs and heterotrophs.

Stock M, Hoefman S, Kerckhof FM, Boon N, De Vos P, De Baets B, Heylen K, Waegeman W.

Res Microbiol. 2013 Dec;164(10):1045-54. doi: 10.1016/j.resmic.2013.08.006. Epub 2013 Sep 4.

PMID:
24012541
20.

A kernel-based model to predict interaction between methanotrophic and heterotrophic bacteria.

Stock M, Hoefman S, Kerckhof FM, Boon N, De Vos P, Heylen K, De Baets B, Waegeman W.

Commun Agric Appl Biol Sci. 2013;78(1):55-60. No abstract available.

PMID:
23875298
21.

Combined exposure to cyanobacteria and carbaryl results in antagonistic effects on the reproduction of Daphnia pulex.

Asselman J, Meys J, Waegeman W, De Baets B, De Schamphelaere KA.

Environ Toxicol Chem. 2013 Sep;32(9):2153-8. doi: 10.1002/etc.2296. Epub 2013 Jul 19.

PMID:
23733205
22.

The logistic curve as a tool to describe the daily ruminal pH pattern and its link with milk fatty acids.

Colman E, Tas BM, Waegeman W, De Baets B, Fievez V.

J Dairy Sci. 2012 Oct;95(10):5845-65. doi: 10.3168/jds.2011-5130. Epub 2012 Aug 9. Erratum in: J Dairy Sci. 2013 Feb;96(2):1323.

23.

Toward a Reliable Evaluation of Forecasting Systems for Plant Diseases: A Case Study Using Fusarium Head Blight of Wheat.

Landschoot S, Waegeman W, Audenaert K, Vandepitte J, Haesaert G, De Baets B.

Plant Dis. 2012 Jun;96(6):889-896. doi: 10.1094/PDIS-08-11-0665.

PMID:
30727362
24.

Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning.

De Bruyne K, Slabbinck B, Waegeman W, Vauterin P, De Baets B, Vandamme P.

Syst Appl Microbiol. 2011 Feb;34(1):20-9. doi: 10.1016/j.syapm.2010.11.003. Epub 2011 Feb 4.

PMID:
21295428
25.

On the role of cost-sensitive learning in multi-class brain-computer interfaces.

Devlaminck D, Waegeman W, Wyns B, Otte G, Santens P.

Biomed Tech (Berl). 2010 Jun;55(3):163-72. doi: 10.1515/BMT.2010.015.

PMID:
20470224
26.

From learning taxonomies to phylogenetic learning: integration of 16S rRNA gene data into FAME-based bacterial classification.

Slabbinck B, Waegeman W, Dawyndt P, De Vos P, De Baets B.

BMC Bioinformatics. 2010 Jan 30;11:69. doi: 10.1186/1471-2105-11-69.

27.

Learning layered ranking functions with structured support vector machines.

Waegeman W, De Baets B, Boullart L.

Neural Netw. 2008 Dec;21(10):1511-23. doi: 10.1016/j.neunet.2008.07.008. Epub 2008 Aug 20.

PMID:
18804954
28.

Polymorphisms in the ficolin 1 gene (FCN1) are associated with susceptibility to the development of rheumatoid arthritis.

Vander Cruyssen B, Nuytinck L, Boullart L, Elewaut D, Waegeman W, Van Thielen M, De Meester E, Lebeer K, Rossau R, De Keyser F.

Rheumatology (Oxford). 2007 Dec;46(12):1792-5.

PMID:
18032536

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