ProteomicsML: An Online Platform for Community-Curated Datasets and Tutorials for Machine Learning in Proteomics Article Swipe
Tobias Greisager Rehfeldt
,
Ralf Gabriels
,
Robbin Bouwmeester
,
Siegfried Gessulat
,
Benjamin A. Neely
,
Magnus Palmblad
,
Yasset Pérez‐Riverol
,
Tobias Schmidt
,
Juan Antonio Vizcaíno
,
Eric W. Deutsch
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2022-2s6kx
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2022-2s6kx
Dataset acquisition and curation are often the hardest and most time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based LC-IM-MS datasets, due to the high-throughput data structure with high levels of noise and complexity between raw and machine learning-ready formats. While predictive proteomics is a field on the rise, when predicting peptide behavior in LC-IM-MS setups, each lab often uses unique and complex data processing pipelines in order to maximize performance, at the cost of accessibility and reproducibility. For this reason we introduce ProteomicsML, an online resource for proteomics-based datasets and tutorials across most of the currently explored physicochemical peptide properties. This community-driven resource makes it simple to access data in easy-to-process formats, and contains easy-to-follow tutorials that allow new users to interact with even the most advanced algorithms in the field. ProteomicsML provides datasets that are useful for comparing state-of-the-art (SOTA) machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available on https://www.proteomicsml.org/ and we welcome the entire proteomics community to contribute to the project at https://github.com/proteomicsml/.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2022-2s6kx
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/633c51a2ea6a223bde08c5df/original/proteomics-ml-an-online-platform-for-community-curated-datasets-and-tutorials-for-machine-learning-in-proteomics.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4301595800
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4301595800Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2022-2s6kxDigital Object Identifier
- Title
-
ProteomicsML: An Online Platform for Community-Curated Datasets and Tutorials for Machine Learning in ProteomicsWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-10-05Full publication date if available
- Authors
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Tobias Greisager Rehfeldt, Ralf Gabriels, Robbin Bouwmeester, Siegfried Gessulat, Benjamin A. Neely, Magnus Palmblad, Yasset Pérez‐Riverol, Tobias Schmidt, Juan Antonio Vizcaíno, Eric W. DeutschList of authors in order
- Landing page
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https://doi.org/10.26434/chemrxiv-2022-2s6kxPublisher landing page
- PDF URL
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/633c51a2ea6a223bde08c5df/original/proteomics-ml-an-online-platform-for-community-curated-datasets-and-tutorials-for-machine-learning-in-proteomics.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/633c51a2ea6a223bde08c5df/original/proteomics-ml-an-online-platform-for-community-curated-datasets-and-tutorials-for-machine-learning-in-proteomics.pdfDirect OA link when available
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Computer science, Field (mathematics), Resource (disambiguation), Pipeline (software), Process (computing), Machine learning, Proteomics, Data science, Artificial intelligence, Raw data, Biochemistry, Gene, Mathematics, Operating system, Pure mathematics, Computer network, Programming language, ChemistryTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2024: 2, 2022: 1Per-year citation counts (last 5 years)
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27Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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