Bram Slabbinck

Bioinformatician (Group member from 2010 until 2013) + 32 (0) 9 33 13 537 VIB / Ghent University Bioinformatics & Systems Biology Technologiepark 927 B-9052 Gent BELGIUM

CV

• 2010 - present: Computational biologist/bioinformatics developer. Bioinformatics & Evolutionary Genomics, VIB, Belgium
• 2006-2009: PhD in Applied Biological Sciences. Thesis title: From learning taxonomies to phylogenetic learning: a computational approach to FAME-based bacterial species identification. Promotors: Bernard De Baets, Paul De Vos & Peter Dawyndt. Faculty of Bioscience Engineering, Ghent University, Belgium. (BELSPO, IUAP)
• 2004-2005: Master in Applied Informatics (Subsequent Master). Faculty of Engineering, Ghent University, Belgium.
• 1999-2004: Master in Bioscience Engineering: Cell and Gene Biotechnology. Faculty of Bioscience Engineering, Ghent University, Belgium.

Papers

(11) Schmidt, M., Van Bel, M., Woloszynska, M., Slabbinck, B., Martens, C., De Block, J., Coppens, F., Van Lijsebettens, M. (2017) Plant-RRBS, a bisulfite and next-generation sequencing-based methylome profiling method enriching for coverage of cytosine positions . BMC Plant Biology (In Press).

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(10) Baute, J., Herman, D., Coppens, F., De Block, J., Slabbinck, B., Dell'Acqua, M., Pè, M.E., Maere, S., Nelissen, B., Inzé, D. (2016) Combined large-scale phenotyping and transcriptomics in maize reveals a robust growth regulatory network. Plant Physiol. 170(3):1848-67.

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(9) Baute, J., Herman, D., Coppens, F., De Block, J., Slabbinck, B., Dell'Acqua, M., Pè, M.E., Maere, S., Nelissen, B., Inzé, D. (2015) Correlation analysis of the transcriptome of growing leaves with mature leaf parameters in a maize RIL population. Genome Biol. 16:168.

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(8) Verkest, A., Byzova, M., Martens, C., Willems, A., Verwulgen, T., Slabbinck, B., Rombaut, D., Van de Velde, J., Vandepoele, K., Standaert, E., Peeters, M., Van Lijsebettens, M., Van Breusegem, F., De Block, J. (2015) Selection for Improved Energy Use Efficiency and Drought Tolerance in Canola Results in Distinct Transcriptome and Epigenome Changes. Plant Physiol. 168(4):1338-50.

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(7) Waegeman, W. ., Verwaeren, J., Slabbinck, B., De Baets, B. (2011) Supervised learning algorithms for multi-class classification problems with partial class memberships. Fuzzy Sets Syst. 184(1):106–125.

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(6) De Bruyne, K., Slabbinck, B., Waegeman, W. ., Vauterin, P., De Baets, B., Vandamme, P. (2011) Bacterial species identification from MALDI-TOF mass spectra through data analysis and machine learning. Syst. Appl. Microbiol. 34(1):20-9.

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

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(4) Slabbinck, B., De Baets, B., Dawyndt, P., De Vos, P. (2010) Analysis of plant-pathogenic pseudomonas species using intelligent learning methods: a general focus on taxonomy and fatty acid analysis within the genus Pseudomonas. Rev. Mex. Fitopatol. 28(1):1-16.

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(3) Slabbinck, B., De Baets, B., Dawyndt, P., De Vos, P. (2009) Towards large-scale FAME-based bacterial species identification using machine learning techniques. Syst. Appl. Microbiol. 32(3):163-76.

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(2) Slabbinck, B., Dawyndt, P., Martens, M., De Vos, P., De Baets, B. (2008) TaxonGap: a visualization tool for intra- and inter-species variation among individual biomarkers. Bioinformatics 24(6):866-7.

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(1) Slabbinck, B., De Baets, B., Dawyndt, P., De Vos, P. (2008) Genus-wide Bacillus species identification through proper artificial neural network experiments on fatty acid profiles. Antonie Van Leeuwenhoek 94(2):187-98.

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