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”  which resulted from the previous iteration from this seminar. The official report from the last seminar is published in the Dagstuhl Reports series .
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.
- 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)
- Artificial Intelligence
- Emerging Technologies
- Information Retrieval