Dagstuhl Seminar 24181
Computational Metabolomics: Towards Molecules, Models, and their Meaning
( Apr 28 – May 03, 2024 )
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Organizers
- Timothy Ebbels (Imperial College London, GB)
- Soha Hassoun (Tufts University - Medford, US)
- Ewy Mathé (National Institutes of Health - Bethesda, US)
- Justin van der Hooft (Wageningen University & Research, NL)
Contact
- Andreas Dolzmann (for scientific matters)
- Christina Schwarz (for administrative matters)
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Metabolomic data, usually from mass spectrometry or nuclear magnetic resonance spectroscopy, is highly complex and information rich. With continuing advances in metabolite detection technologies, the volume and complexity of data is ever increasing. Computational analysis of metabolomic data, from raw data processing to biological interpretation, is therefore fundamental to realizing the impact of metabolomics on a diverse array of application fields, e.g., environmental toxicity, industrial biotechnology, and biomedicine. This Dagstuhl Seminar extends the Computational Metabolomics series to examine how we can enhance the utility and interpretation of metabolomics data and will include three major themes:
- Enhancing the utility of large scale metabolomic data through application of state-of-the-art ML/DL methods, networks, standardization, and data sharing;
- Evaluating the robustness and increasing confidence in ML/DL modeling by incorporating experimental measures and producing interpretable metrics;
- Empowering the research community in interpreting metabolomic and multi-omic data through big data and knowledge sources analytics using public repositories.
This seminar will thus bring together a multidisciplinary set of computationally focused scientists to address the challenges and opportunities emerging in this important and rapidly changing field.
We aim to bring together many disciplines, including mass spectrometrists and NMR spectroscopists, computer scientists, biostatisticians, epidemiologists, biologists, and chemists. This Dagstuhl Seminar series has a strong record of collaborative outputs that result from gathering multi-disciplinary experts who contribute to computational metabolomics, directly or indirectly. These include new collaborations, grant applications, software tools, online resources and papers, addressing the key challenges identified in the field and discussed at the meeting. As an example, please see the paper “Recent advances in mass spectrometry-based computational metabolomics” [1] which resulted from the previous iteration from this seminar. The official report from the last seminar is published in the Dagstuhl Reports series [2].
The seminar organization will follow a flexible format, as typical in Dagstuhl Seminars. The discussions will center around a list of proposed topics and some that will be added after pre-meeting and brainstorming discussions. We will also consider short overview presentations when the discussion involves members with particularly diverse background expertise. Additionally, we plan to conduct several new pre-meeting Slack activities to solicit topics, collect relevant publications, and create a community around the seminar. As always, flexibility will be critical in enabling conversation to guide the seminar direction.
[1] https://www.sciencedirect.com/science/article/pii/S1367593123000261
[2] https://doi.org/10.4230/DagRep.12.5.1
Creative Commons BY 4.0
Timothy Ebbels, Soha Hassoun, Ewy Mathé, and Justin van der Hooft
- Wout Bittremieux (University of Antwerp, BE)
- Sebastian Böcker (Friedrich-Schiller-Universität Jena, DE) [dblp]
- Carl Brunius (Chalmers University of Technology - Göteborg, SE)
- Roman Bushuiev (The Czech Academy of Sciences - Prague, CZ)
- Haley Chatelaine (NCATS - Bethesda, US)
- Ronan Daly (University of Glasgow - Bearsden, GB)
- Niek de Jonge (Wageningen University, NL)
- Kai Dührkop (Friedrich-Schiller-Universität Jena, DE) [dblp]
- Timothy Ebbels (Imperial College London, GB)
- Soha Hassoun (Tufts University - Medford, US)
- Florian Huber (Hochschule Düsseldorf, DE)
- Pär Jonsson (Sartorius Stedim Data Analytics - Umeå, SE)
- Purva Kulkarni (Radboud University - Nijemgen, NL)
- Jessica Lasky-Su (Brigham and Women's Hospital & Harvard Medical School - Boston, US)
- Alice Limonciel (biocrates life sciences - Innsbruck, AT)
- Liping Liu (Tufts University - Medford, US)
- Tytus Mak (NIST - Gaithersburg, US)
- Ewy Mathé (National Institutes of Health - Bethesda, US) [dblp]
- Hosein Mohimani (Carnegie Mellon University - Pittsburgh, US)
- María Eugenia Monge (CIBION - Buenos Aires, AR)
- Steffen Neumann (IPB - Halle, DE) [dblp]
- Louis-Felix Nothias (CNRS & Université Côte d'Azur - Nice, FR)
- Daniel Raftery (University of Washington - Seattle, US)
- Raphael Reher (Universität Marburg, DE)
- Stacey N. Reinke (Edith Cowan University - Joondalup, AU)
- Hannes Röst (University of Toronto, CA) [dblp]
- Juho Rousu (Aalto University, FI) [dblp]
- Robin Schmid (The Czech Academy of Sciences - Prague, CZ)
- Emma Schymanski (University of Luxembourg, LU) [dblp]
- Denise Slenter (Maastricht University, NL)
- Jan Stanstrup (University of Copenhagen, DK)
- Michael Andrej Stravs (Eawag - Dübendorf, CH)
- Marynka Ulaszewska-Tarantino (Thermo Fisher Scientific - Milan, IT)
- Justin van der Hooft (Wageningen University & Research, NL) [dblp]
- Dries Verdegem (VIB - KU Leuven, BE)
- Juan Antonio Vizcaino (EMBL-EBI - Hinxton, GB) [dblp]
- Mingxun Wang (University of California - Riverside, US)
- Ralf Weber (University of Birmingham, GB)
- Egon Willighagen (Maastricht University, NL)
- David Wishart (University of Alberta - Edmonton, CA) [dblp]
- Michael Anton Witting (Helmholtz Zentrum München, DE)
- Nicola Zamboni (ETH Zürich, CH) [dblp]
Related Seminars
- Dagstuhl Seminar 15492: Computational Metabolomics (2015-11-29 - 2015-12-04) (Details)
- Dagstuhl Seminar 17491: Computational Metabolomics: Identification, Interpretation, Imaging (2017-12-03 - 2017-12-08) (Details)
- Dagstuhl Seminar 20051: Computational Metabolomics: From Cheminformatics to Machine Learning (2020-01-26 - 2020-01-31) (Details)
- Dagstuhl Seminar 22181: Computational Metabolomics: From Spectra to Knowledge (2022-05-01 - 2022-05-06) (Details)
Classification
- Artificial Intelligence
- Emerging Technologies
- Information Retrieval
Keywords
- metabolomics
- bioinformatics
- cheminformatics
- multi-omics
- benchmarking
