License: 
Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.10.1.144
URN: urn:nbn:de:0030-drops-124036
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/12403/
Böcker, Sebastian ;
Broeckling, Corey ;
Schymanski, Emma ;
Zamboni, Nicola
Weitere Beteiligte (Hrsg. etc.): Sebastian Böcker and Corey Broeckling and Emma Schymanski and Nicola Zamboni
Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)
Abstract
Dagstuhl Seminar 20051 on Computational Metabolomics is the third edition of seminars on this topic and focused on Cheminformatics and Machine Learning. With the advent of higher precision instrumentation, application of metabolomics to a wider variety of small molecules, and ever increasing amounts of raw and processed data available, developments in cheminformatics and machine learning are sorely needed to facilitate interoperability and leverage further insights from these data. Following on from Seminars 17491 and 15492, this edition convened both experimental and computational experts, many of whom had attended the previous sessions and brought much-valued perspective to the week’s proceedings and discussions. Throughout the week, participants first debated on what topics to discuss in detail, before dispersing into smaller, focused working groups for more in-depth discussions. This dynamic format was found to be most productive and ensured active engagement amongst the participants. The abstracts in this report reflect these working group discussions, in addition to summarising several informal evening sessions. Action points to follow-up on after the seminar were also discussed, including future workshops and possibly another Dagstuhl seminar in late 2021 or 2022.
BibTeX - Entry
@Article{bcker_et_al:DR:2020:12403,
author = {Sebastian B{\"o}cker and Corey Broeckling and Emma Schymanski and Nicola Zamboni},
title = {{Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)}},
pages = {144--159},
journal = {Dagstuhl Reports},
ISSN = {2192-5283},
year = {2020},
volume = {10},
number = {1},
editor = {Sebastian B{\"o}cker and Corey Broeckling and Emma Schymanski and Nicola Zamboni},
publisher = {Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2020/12403},
URN = {urn:nbn:de:0030-drops-124036},
doi = {10.4230/DagRep.10.1.144},
annote = {Keywords: bioinformatics, chemoinformatics, computational mass spectrometry, computational metabolomics, machine learning}
}
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Keywords: |
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bioinformatics, chemoinformatics, computational mass spectrometry, computational metabolomics, machine learning |
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Collection: |
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Dagstuhl Reports, Volume 10, Issue 1 |
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Issue Date: |
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2020 |
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Date of publication: |
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26.06.2020 |
