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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 deep learning's role in proteomics research, highlighting interdisciplinary convergence and key trends.

Key Points

  • The study identifies exponential growth in publications since 2010, with an average annual rate of 12.53%.
  • Key themes include deep learning algorithms and multi-omics analysis, as highlighted by the popularity of AlphaFold2-related research.
  • Bibliometric tools were employed to analyze over 5,000 publications, focusing on citation networks and research trends.
  • Findings emphasize the importance of interdisciplinary collaboration to enhance precision medicine in proteomics.

Cite This Study

Tan et al. (2025) studied this question.

synapsesocial.com/papers/68af4965ad7bf08b1ead5d47https://doi.org/10.3389/fmed.2025.1594442
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