ProteomicsML provides ready-made datasets for machine learning models accompanied by tutorials on how to work with even the most complex data types in the field of proteomics. The resource is set up to evolve together with the field, and we welcome everyone to contribute to the project by adding new datasets and accompanying notebooks.

ProteomicsML was set up as a joint effort of SDU, CompOmics, LUMC, PeptideAtlas, NIST, PRIDE, and MSAID. We believe that ProteomicsML is solid step forward for the field towards more open and reproducible science!

Want to learn more about the project? Read our publication:

ProteomicsML: An Online Platform for Community-Curated Data Sets and Tutorials for Machine Learning in Proteomics.
Tobias G. Rehfeldt*, Ralf Gabriels*, Robbin Bouwmeester*, Siegfried Gessulat, Benjamin A. Neely, Magnus Palmblad, Yasset Perez-Riverol, Tobias Schmidt, Juan Antonio Vizcaı́no§, and Eric W. Deutsch§.
J. Proteome Res. 2023, 22, 2, 632–636. doi:10.1021/acs.jproteome.2c00629.

📒 Explore all tutorials and datasets
🙏 Ask or answer questions about the tutorials in Tutorials Q&A

📄 Discuss the existing datasets or the addition of a new dataset in Dataset Discussions
💬 Join the ProteomicsML General Discussions

💡 Have an idea on how to improve the project? Open an issue
🧑‍🔧 Learn how to Contribute
🤝 Read the Code of Conduct