Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.

Objective: Despite growing interest in the application of machine learning (ML) in proteomics, a comprehensive and systematic mapping of this research domain has been lacking. This study addresses this gap by conducting the first large-scale bibliometric analysis focused exclusively on ML-driven proteomics, aiming to elucidate its knowledge structure, development trajectory, and emerging research trends. Methods: A total of 5,156 publications from the Web of Science Core Collection (1997-2024) were retrieved and analyzed. Bibliometric tools including CiteSpace 6.4.R1, VOSviewer 1.6.18, Scimago Graphica, and the R package bibliometrix were used to extract and visualize key bibliometric indicators. After data cleaning and de-duplication, analyses were conducted on keyword co-occurrence, citation networks, leading journals, influential authors, and institutional collaboration patterns to construct a comprehensive landscape of ML applications in proteomics. Results: The number of publications has grown exponentially since 2010, with an average annual growth rate of 12.53% and a notable surge of 65.14% occurring between 2019 and 2020. The United States emerged as the most productive country, while the Chinese Academy of Sciences led among institutions. AlphaFold2-related research received the highest citations, reflecting the transformative role of deep learning in protein structure prediction. Thematic clustering revealed key research foci, including deep learning algorithms, protein-protein interaction prediction, and integrative multi-omics analysis. The field is characterized by strong interdisciplinary convergence, involving computer science, molecular biology, and clinical research. High-impact journals and influential authors were also identified, providing benchmarks for academic influence and collaboration. Conclusion: This study offers the first comprehensive bibliometric analysis of ML in proteomics, revealing key themes such as deep learning, pretrained models, and multi-omics integration. Future efforts should focus on building interpretable models, enhancing cross-disciplinary collaboration, and ensuring secure, standardized data use to advance precision medicine. Systematic review registration: https://doi.org/10.17605/OSF.IO/F4WUG.