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Documents authored by Schymanski, Emma

Document
DOI: 10.4230/DagRep.10.1.144
Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051)

Authors: Sebastian Böcker, Corey Broeckling, Emma Schymanski, and Nicola Zamboni

Published in: Dagstuhl Reports, Volume 10, Issue 1 (2020)

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.
Cite as

Sebastian Böcker, Corey Broeckling, Emma Schymanski, and Nicola Zamboni. Computational Metabolomics: From Cheminformatics to Machine Learning (Dagstuhl Seminar 20051). In Dagstuhl Reports, Volume 10, Issue 1, pp. 144-159, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@Article{bocker_et_al:DagRep.10.1.144,
  author =	{B\"{o}cker, Sebastian and Broeckling, Corey and Schymanski, Emma and Zamboni, Nicola},
  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 =	{B\"{o}cker, Sebastian and Broeckling, Corey and Schymanski, Emma and Zamboni, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.10.1.144},
  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}
}
Document
DOI: 10.4230/DagRep.7.12.1
Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491)

Authors: Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski

Published in: Dagstuhl Reports, Volume 7, Issue 12 (2018)

Abstract
Metabolites are key players in almost all biological processes, and play various functional roles providing energy, building blocks, signaling, communication, and defense. Metabolites serve as clinical biomarkers for detecting medical conditions such as cancer; small molecule drugs account for 90% of prescribed therapeutics. Complete understanding of biological systems requires detecting and interpreting the metabolome in time and space. Following in the steps of high-throughput sequencing, mass spectrometry (MS) has become established as a key analytical technique for large-scale studies of complex metabolite mixtures. MS-based experiments generate datasets of increasing complexity and size. The Dagstuhl Seminar on Computational Metabolomics brought together leading experts from the experimental (analytical chemistry and biology) and the computational (computer science and bioinformatics) side, to foster the exchange of expertise needed to advance computational metabolomics. The focus was on a dynamic schedule with overview talks followed by break-out sessions, selected by the participants, covering the whole experimental-computational continuum in mass spectrometry. Particular focus in this seminar was given to imaging mass spectrometry techniques that integrate a spacial component into the analysis, ranging in scale from single cells to organs and organisms.
Cite as

Theodore Alexandrov, Sebastian Böcker, Pieter Dorrestein, and Emma Schymanski. Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491). In Dagstuhl Reports, Volume 7, Issue 12, pp. 1-17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{alexandrov_et_al:DagRep.7.12.1,
  author =	{Alexandrov, Theodore and B\"{o}cker, Sebastian and Dorrestein, Pieter and Schymanski, Emma},
  title =	{{Computational Metabolomics: Identification, Interpretation, Imaging (Dagstuhl Seminar 17491)}},
  pages =	{1--17},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2018},
  volume =	{7},
  number =	{12},
  editor =	{Alexandrov, Theodore and B\"{o}cker, Sebastian and Dorrestein, Pieter and Schymanski, Emma},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.7.12.1},
  URN =		{urn:nbn:de:0030-drops-86740},
  doi =		{10.4230/DagRep.7.12.1},
  annote =	{Keywords: algorithms, bioinformatics, cheminformatics, computational mass spectrometry, computational metabolomics, databases, imaging mass spectrometry}
}
Document
DOI: 10.4230/DagRep.5.11.180
Computational Metabolomics (Dagstuhl Seminar 15492)

Authors: Sebastian Böcker, Juho Rousu, and Emma Schymanski

Published in: Dagstuhl Reports, Volume 5, Issue 11 (2016)

Abstract
he Dagstuhl Seminar 15492 on Computational Metabolomics brought together leading experimental (analytical chemistry and biology) and computational (computer science and bioinformatics) experts with the aim to foster the exchange of expertise needed to advance computational metabolomics. The focus was on a dynamic schedule with overview talks followed by breakout sessions, selected by the participants, covering the whole experimental-computational continuum in mass spectrometry, as well as the use of metabolomics data in applications. A general observation was that metabolomics is in the state that genomics was 20 years ago and that while the availability of data is holding back progress, several good initiatives are present. The importance of small molecules to life should be communicated properly to assist initiating a global metabolomics initiative, such as the Human Genome project. Several follow-ups were discussed, including workshops, hackathons, joint paper(s) and a new Dagstuhl Seminar in two years to follow up on this one.
Cite as

Sebastian Böcker, Juho Rousu, and Emma Schymanski. Computational Metabolomics (Dagstuhl Seminar 15492). In Dagstuhl Reports, Volume 5, Issue 11, pp. 180-192, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@Article{bocker_et_al:DagRep.5.11.180,
  author =	{B\"{o}cker, Sebastian and Rousu, Juho and Schymanski, Emma},
  title =	{{Computational Metabolomics (Dagstuhl Seminar 15492)}},
  pages =	{180--192},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2016},
  volume =	{5},
  number =	{11},
  editor =	{B\"{o}cker, Sebastian and Rousu, Juho and Schymanski, Emma},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.5.11.180},
  URN =		{urn:nbn:de:0030-drops-58016},
  doi =		{10.4230/DagRep.5.11.180},
  annote =	{Keywords: algorithms, bioinformatics, cheminformatics, computational mass spectrometry, computational metabolomics, databases}
}
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