ArticleProceedings of the National Academy of Sciences of the United States of America2020
Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 113 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
113 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-Analysis and Machine Learning Prediction of Protein Corona Composition across Nanoparticle Systems in Biological Media.ACS nano · 2025Pooled it
- From RNA design to delivery: Computational strategies for functional RNA therapeutics.Bioactive materials · 2027Review
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment.Molecules (Basel, Switzerland) · 2026Review
- Biologically Mediated Nanoparticle Synthesis as a Potential Green Strategy: Principles, Methods, and Pharmaceutical Applications.Pharmaceutics · 2026Review
- Multidimensional Protein Corona Analysis Toward Predictive Nano-Bio Interface Design.Small methods · 2026Review
- Applications of synthetic biology in biomedicine.Molecular biomedicine · 2026Review
- Delivering the future of immunotherapy: A state-of-the-art review of gene editing in immune cells with lipid nanoparticles.Materials today. Bio · 2026Review
- Emerging Nanobiochar -Hydrogel Therapeutic Systems: Redox Modulation, Biointerface Interactions, and Critical Gaps in In Vitro Evaluation.Gels (Basel, Switzerland) · 2026Review
- Revisiting Colloid Theory for Biomedicine: Reconciling Simple Models in Complex Environments.ACS nano medicine · 2026Article
- A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research.ACS environmental Au · 2026Article
- Delivery Systems for Therapeutic Genome Editing: Challenges, Innovations, and Future Perspectives.MedComm · 2026Review
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases.Discover nano · 2026Review
- Smart Nanodelivery Systems for Immunometabolic Modulation in Osteoarthritis.Exploration (Beijing, China) · 2026Review
- Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.Scientific reports · 2026Article
- The protein corona at the nano-bio interface: the need for standardized methodology and opportunities for neurodegenerative disease intervention.RSC advances · 2026Review
- Monocyte/macrophage-mediated trained immunity in disease prevention and immunotherapy.Bioactive materials · 2026Review
- Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.Nanomedicine (London, England) · 2026Review
- Artificial intelligence empowered biomaterials for cancer therapy: From rational design to clinical translation.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
53 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein corona formation is critical for the design of ideal and safe nanoparticles (NPs) for nanomedicine, biosensing, organ targeting, and other applications, but methods to quantitatively predict the formation of the protein corona, especially for functional compositions, remain unavailable. The traditional linear regression model performs poorly for the protein corona, as measured by
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.