Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 7, 2025Expert Review of Proteomics

A 2025 perspective on the role of machine learning for biomarker discovery in clinical proteomics

View Full Paper
Ask AI
Bookmark
Share

Authors

CACharlotte AdamsWBWout Bittremieux

Discussion

Loading...

Member takes

Overview

Perspective highlights challenges in biomarker discovery through machine learning in clinical proteomics, suggesting better practices.

Key Points

  • Machine learning shows promise for biomarker discovery but faces limitations in clinical proteomics.
  • Common issues like overfitting and data leakage hinder machine learning models in real-life applications.
  • Adopting simpler models may yield better interpretability and performance compared to complex architectures.
  • Emphasizing rigorous design and validation can enhance the translational impact of machine learning in clinical settings.

Cite This Study

Adams et al. (2025) studied this question.

synapsesocial.com/papers/689dfe90d61984b91e13bb3fhttps://doi.org/10.1080/14789450.2025.2545828
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning enhances biomarker discovery: From multi- omics to functional genomics.2025
  2. 2AI and Machine Learning for Proteomics-Driven Drug Discovery: Methods, Tools, and Best Practices2026 · 7 citations
  3. 3Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics2024 · 1 citations
  4. 4Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics2024 · 18 citations
  5. 5Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis2025 · 4 citations