Robbin Bouwmeester, VIB-UGent
Robbin Bouwmeester finished his M.Sc. in Bioinformatics at the VU University Amsterdam. After his M.Sc., he joined the CompOmics group in Ghent (Belgium) as a PhD student, and was part of the ITN project called MASSTRPLAN.
In this project the focus was on developing data-driven methodologies for the identification of lipid oxidation products and lipoxidation. To improve identification of these analytes, Machine Learning and Deep Learning models were developed for the simulation and interpretation of IM-LC-MS data.
Boosting the analysis of IM-LC-MS data with Machine Learning
Accurate prediction of liquid chromatographic retention times and collisional cross section of metabolites is useful for better identification in untargeted MS and limiting experimental measurements in targeted MS. For example, retention time predictions have been applied to untargeted MS experiments to identify the correct lipids from multiple isobaric candidates. Even though proven useful, these predictions are not commonly applied in the analysis. This is mostly due to differences in experimental setups and as a result an incompatibility of models (differences in columns, gradients, solvents, stationary phase, etc.). We introduce the concept of generalized calibration. The key idea of this concept is to fit calibration curves on predictions from setup specific models. The calibrated predictions are then blended using machine learning for more accurate predictions on the experimental setup of interest.
Benelux Metabolomics Days 2020Registration website for Benelux Metabolomics Days 2020
Benelux Metabolomics Days 2020Benelux Metabolomics Days 20200.00EUROnlineOnly2019-01-01T00:00:00Z
Engels Conference CenterEngels Conference CenterStationsplein 45 3013 AK Rotterdam Netherlands