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August 15, 2025Frontiers in MedicineOpen Access

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

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Authors

CTChao TanHLHao LiuZZZhen Zhang

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Overview

Bibliometric analysis reveals exponential growth and key trends in machine learning applications in proteomics, indicating deep learning's transformative impact.

Key Points

  • The number of publications in ML-driven proteomics has increased exponentially since 2010, showcasing clear growth trends.
  • A notable 65.14% surge in publications occurred between 2019 and 2020, establishing a pivotal moment in this research domain.
  • Analysis included keyword co-occurrence and citation networks, highlighting themes like deep learning algorithms and protein structure prediction.
  • The findings suggest a need for future work on interpretable models and standardized data use to enhance precision medicine.

Cite This Study

Tan et al. (2025) studied this question.

synapsesocial.com/papers/68af3e3ccf1dd9ea359eaf2dhttps://doi.org/10.3389/fmed.2025.1594442
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