ArticleJournal of proteome research2023
ProteomicsML: An Online Platform for Community-Curated Data sets and Tutorials for Machine Learning in Proteomics.
Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- AI proteomics: from protein identification to virtual cells.Nature methods · 2026Review
- Cross-domain transfer learning from peptides to metabolites using a multi-property fine-tuned LLM.Bioinformatics (Oxford, England) · 2026Article
- Review
- Transfer learning in DeepLC improves LC retention time prediction across substantially different modifications and setups.Nature communications · 2026Article
- Better Inputs, Better Learning: A Peptide Embedding Tutorial for Proteomic Mass Spectrometry.Journal of proteome research · 2026Article
- The ProteomeXchange consortium in 2026: making proteomics data FAIR.Nucleic acids research · 2026Article
- Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.Proteomics · 2025Review
- Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics.Analytical chemistry · 2025Review
- The PRIDE database at 20 years: 2025 update.Nucleic acids research · 2025Article
- Perspectives in computational mass spectrometry: recent developments and key challenges.Bioinformatics advances · 2025Article
- Attracting Computational Researchers to Proteomics.Journal of the American Society for Mass Spectrometry · 2024Article
- The Role of Clinical Glyco(proteo)mics in Precision Medicine.Molecular & cellular proteomics : MCP · 2023Article
- Toward an Integrated Machine Learning Model of a Proteomics Experiment.Journal of proteome research · 2023Review
- Variability analysis of LC-MS experimental factors and their impact on machine learning.GigaScience · 2022Article
- Proteomic repository data submission, dissemination, and reuse: key messages.Expert review of proteomicsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 5 countries.
Funding
Abstract
Data set acquisition and curation are often the most difficult and time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based liquid chromatography (LC) coupled to mass spectrometry (MS) data sets, due to the high levels of data reduction that occur between raw data and machine learning-ready data. Since predictive proteomics is an emerging field, when predicting peptide behavior in LC-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 data sets 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 data sets that are useful for comparing state-of-the-art machine learning algorithms, as well as providing introductory material for teachers and newcomers to the field alike. The platform is freely available at https://www.proteomicsml.org/, and we welcome the entire proteomics community to contribute to the project at https://github.com/ProteomicsML/ProteomicsML.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.