ArticleRegenerative biomaterials2024
Unveiling protein corona composition: predicting with resampling embedding and machine learning.
Article in Regenerative biomaterials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Biomaterials at the interface of bone-derived factors and inter-organ communication: Current evidence and future perspectives.Bioactive materials · 2026Review
- Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.Scientific reports · 2026Article
- Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.Nanomedicine (London, England) · 2026Review
- Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development.Pharmaceutical research · 2026Review
- In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review.Computational and structural biotechnology journal · 2026Review
- The Challenges and Opportunities of Protein Coronas for Nanoscale Biomolecular Sensing.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- Integrating graph convolutional networks with large language models for structured biomedical material knowledge representation.Regenerative biomaterials · 2025Article
- AI-driven biomaterial design: an intelligent closed loop from reverse design to biological response.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (
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Registered trials
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