MSc Graduates

Name Title Date
J.-H. NowéCountering illegal timbering by using multimodal machine learning2022 - 2023
N. TourneTwo-branch neural networks for predicting protein-DNA interaction2022 - 2023
Y. Van LaereDetection of 5mC modification in Nanopore sequencing data using deep learning2021 - 2022
E. LorrezQuantifying the environmental controls on African tropical forest dynamics through using a Granger causality framework2020 - 2021
L. TheunissenA comparison of flat and hierarchical classification for automatic annotation of single-cell transcriptomics data2020 - 2021
T. WillaertImproving classification by rejection of high epistemic uncertainty points2020 - 2021
K. ZhangDrug-target interaction prediction using multi-target prediction methods2020 - 2021
L. DaveyUsing artificial neural networks to uncover features in promoter sequences responsible for nonorthogonality in E. Coli2019 - 2020
B. De SaedeleerCombatting illegal timber trade using chemical fingerprints: the power of mathematics and mass spectrometry2019 - 2020
L. PollarisProtein secondary structure prediction using transformer networks2019 - 2020
G. TjonAutomative drinking water monitoring using flow cytometry data2019 - 2020
B. VerfailliePattern recognition in raman spectroscopy data for a faster labelling of subjects in multiple domains2019 - 2020
K. D'haeyerTowards a data-driven identification of the microbial "Rammanome"2018 - 2019
B. De ClercqForecasting tidal surge in the Lower Sea Scheldt using machine learning techniques2018 - 2019
G. De ClercqDeep learning for classification of DNA functional sequences2018 - 2019
N. GoedersFight the illegal wood trade through chemical fingerprints : The power of mathematics and mass spectrometry2018 - 2019
S. TopVertical farming of lettuce: the influence of rhizosphere bacteria and substrates2018 - 2019
M. Van HaeverbekeDetection of m6a modifications in native RNA using Oxford Nanopore Technology2018 - 2019
A. De GraeveDetecting climate drivers for vegetation extremes2017 - 2018
R. IngelsUnderstanding vegetation anomalies with machine learning methods2017 - 2018
M. MisonnePrediction of RNA polymerase-DNA interactions in Escherichia Coli2017 - 2018
T. VanlerbergheHierarchical multi-label classification of food products2017 - 2018
L. BodynExploration of deep autoencoders for collaborative filtering on cooking recipes2016 - 2017
S. DecubberSpatiotemporal optimization of Granger causality methods for climate change attribution 2016 - 2017
J. HeyseDevelopment and application of single-cell analysis tools for the study of sympatric bacterial populations2016 - 2017
T. MortierModeling of climate-vegetation dynamics using machine learning techniques in a non-linear Granger causality framework2016 - 2017
D. SchaumontEen integratieve benadering gebaseerd op random forest voor de verbeterde predictie van exon-intron juncties2016 - 2017
X. YinDiscovering relationships in climate-vegetation dynamics using dynamic feature selection techniques2016 - 2017
C. ZhangVisualization and unsupervised learning of flow cytometry data for bacterial identication2016 - 2017
F. RamonA data-driven analysis of ingredients in cooking recipes2015 - 2016
L. TillemanIn silico engineering van cytochroom P450 via machine learning technieken2015 - 2016
J. De ReuAnalyse van voorspellingsmodellen voor aardappelziekten en hun toepasbaarheid in Vlaanderen2014 - 2015
W.K. TsangAssessing pathogen invasion based on community evenness and metabolic similarity2014 - 2015
M. De ClercqPrediction of ingredient combinations using machine learning techniques2013 - 2014
A. De PaepeThe prediction of interaction between mRNA and miRNA using machine learning techniques2012 - 2013
M. StockLearning pairwise relations in bioinformatics: three case studies2011 - 2012
J. VandepitteVoorspellen van Fusarium spp. aanwezigheid en DON concentraties in wintertarwe met machine learning technieken 2009 - 2010