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 Vizcaíno§

Eric W. Deutsch§


September 30, 2022

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 physico-chemical 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 and we welcome the entire proteomics community to contribute to the project at

Published in the Third Special Issue on Software Tools and Resources of Journal of Proteome Research. Read the full 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.