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Luc Pieters   Dr.  Senior Scientist or Principal Investigator 
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Luc Pieters published an article in April 2019.
Top co-authors See all
Adrian Covaci

602 shared publications

Toxicological Center, Department of Pharmaceutical Sciences, University of Antwerp, 2000 Antwerp, Belgium.

Sang Hyun Sung

226 shared publications

College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Korea

Jean-Luc Wolfender

225 shared publications

School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, CMU, 1, Rue Michel Servet, 1211 Geneva, Switzerland

Hermann Stuppner

214 shared publications

A Institute of Pharmacy/Pharmacognosy, Center for Molecular Biosciences Innsbruck (CMBI), Center for Chemistry and Biomedicine, University of Innsbruck, Innrain 80-82, Innsbruck, 6020 Austria

Leandros A. Skaltsounis

206 shared publications

Department of Pharmacognosy and Natural Products Chemistry, Faculty of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece

286
Publications
6
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403
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Publication Record
Distribution of Articles published per year 
(1985 - 2019)
Total number of journals
published in
 
21
 
Publications See all
Article 0 Reads 0 Citations Chlorogenic Acid as a Model Compound for Optimization of an In Vitro Gut Microbiome-Metabolism Model Olivier Mortelé, Elias Iturrospe, Annelies Breynaert, Christ... Published: 19 April 2019
Proceedings, doi: 10.3390/proceedings2019011031
DOI See at publisher website ABS Show/hide abstract
It has been believed that the metabolism of xenobiotics occurred mainly by the cytochrome P450 enzyme system in the liver. However, recent data clearly suggest a significant role for the gut microbiota in the metabolism of xenobiotic compounds. This microbiotic biotransformation could lead to differences on activation, inactivation and possible toxicity of these compounds. In vitro models are generally used to study the colonic biotransformation as they allow easy dynamic and multiple sampling over time. However, to ensure this accurately mimics communities in vivo, the pre-analytical phase requires optimization. Chlorogenic acid, a polyphenolic compound abundantly present in the human diet, was used as a model compound to optimize a ready-to-use gut microbiome biotransformation platform. Samples of the in vitro gastrointestinal dialysis-model with colon stage were analyzed by liquid chromatography coupled to high resolution time-of-flight mass spectrometry. Complementary screening approaches were also employed to identify the biotransformation products.
Article 0 Reads 0 Citations Antiplasmodial prenylated flavonoids from stem bark of Erythrina latissima Emmy Tuenter, Yancho Zarev, An Matheeussen, Esameldin Elgora... Published: 01 April 2019
Phytochemistry Letters, doi: 10.1016/j.phytol.2019.02.001
DOI See at publisher website
Article 0 Reads 0 Citations Using Expert Driven Machine Learning to Enhance Dynamic Metabolomics Data Analysis. Charlie Beirnaert, Laura Peeters, Pieter Meysman, Wout Bittr... Published: 20 March 2019
Metabolites, doi: 10.3390/metabo9030054
DOI See at publisher website PubMed View at PubMed ABS Show/hide abstract
Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. As untargeted metabolomics datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficiently sophisticated. In addition, the ground truth for untargeted metabolomics experiments is intrinsically unknown and the performance of tools is difficult to evaluate. Here, the problem of dynamic multi-class metabolomics experiments was investigated using a simulated dataset with a known ground truth. This simulated dataset was used to evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning. The results were compared to EDGE, a statistical method for time series data. This paper presents three novel outcomes. The first is a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs. control metabolomics data. Third, the tinderesting method is introduced to analyse more complex dynamic metabolomics experiments. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.
Article 0 Reads 0 Citations HPLC-DAD-SPE-NMR isolation of tetracyclic spiro-alkaloids with antiplasmodial activity from the seeds of Erythrina latis... Yancho Zarev, Kenn Foubert, Paul Cos, Louis Maes, Esameldin ... Published: 03 January 2019
Natural Product Research, doi: 10.1080/14786419.2018.1539976
DOI See at publisher website
Article 0 Reads 0 Citations Isolation and structure elucidation of two antiprotozoal bisbenzylisoquinoline alkaloids from Triclisia gilletii stem ba... R. Cimanga Kanyanga, C. Kikweta Munduku, S. Nsaka Lumpu, M. ... Published: 01 December 2018
Phytochemistry Letters, doi: 10.1016/j.phytol.2018.09.008
DOI See at publisher website
PREPRINT-CONTENT 0 Reads 0 Citations Using expert driven machine learning to enhance dynamic metabolomics data analysis.: Charlie Beirnaert, Laura Peeters, Pieter Meysman, Wout Bittr... Published: 29 November 2018
bioRxiv, doi: 10.1101/482224
DOI See at publisher website ABS Show/hide abstract
Data analysis for metabolomics is undergoing rapid progress thanks to the proliferation of novel tools and the standardization of existing workflows. However, as datasets and experiments continue to increase in size and complexity, standardized workflows are often not sufficient. In addition, as the ground truth for metabolomics experiments is intrinsically unknown, there is no way to critically evaluate the performance of tools. Here, we investigate the problem of dynamic multi-class metabolomics experiments using a simulated dataset with a known ground truth and evaluate the performance of tinderesting, a new and intuitive tool based on gathering expert knowledge to be used in machine learning, and compare it to EDGE, a statistical method for sequence data. This paper presents three novel outcomes. First we present a way to simulate dynamic metabolomics data with a known ground truth based on ordinary differential equations. This method is made available through the MetaboLouise R package. Second, we show that the EDGE tool, originally developed for genomics data analysis, is highly performant in analyzing dynamic case vs control metabolomics data. Last, we introduce the tinderesting method to analyse more complex dynamic metabolomics experiments that performs on par with statistical methods. This tool consists of a Shiny app for collecting expert knowledge, which in turn is used to train a machine learning model to emulate the decision process of the expert. This approach does not replace traditional data analysis workflows for metabolomics, but can provide additional information, improved performance or easier interpretation of results. The advantage is that the tool is agnostic to the complexity of the experiment, and thus is easier to use in advanced setups. All code for the presented analysis, MetaboLouise and tinderesting are freely available.
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