ArticleEnvironmental science. Nano2018
Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties.
Article in Environmental science. Nano, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
45 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
- Bridging Nano-Interface Interactions and Organ-Specific Toxicity: A Review of Machine Learning for Nanomaterials Risk Assessment.Molecules (Basel, Switzerland) · 2026Review
- Revisiting Colloid Theory for Biomedicine: Reconciling Simple Models in Complex Environments.ACS nano medicine · 2026Article
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- From LNPs to hybrid nanocarriers: development, challenges and redesign of non-viral gene delivery.Journal of nanobiotechnology · 2026Review
- Determinants of protein corona adsorption and abundance revealed by interpretable machine learning across nanoparticle systems.Scientific reports · 2026Article
- Navigating theNanoscale advances · 2026Review
- Advancing nanomedicine with machine learning: predicting protein corona and nano-bio interactions.Nanomedicine (London, England) · 2026Review
- Mass Spectrometry Proteomics of the Nanoparticle Corona Is Highly Dependent on Sample Preparation Protocol.Proteomics · 2026Article
- In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review.Computational and structural biotechnology journal · 2026Review
- Decoy Nanoparticles and Protein Corona Modulation: A Novel Frontier in Sepsis Treatment.International journal of nanomedicine · 2026Review
- Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development.Pharmaceutical research · 2026Review
- Biomolecular Transformations Shape the Environmental Fate of Nanoscale and Emerging Materials.Accounts of chemical research · 2025Review
- The Challenges and Opportunities of Protein Coronas for Nanoscale Biomolecular Sensing.Small (Weinheim an der Bergstrasse, Germany) · 2025Review
- Machine Learning-Enhanced Nanoparticle Design for Precision Cancer Drug Delivery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Beyond Correlation: Establishing Causality in Protein Corona Formation for Nanomedicine.Molecular pharmaceutics · 2025Article
- Artificial Intelligence and Computational Biology in Gene Therapy: A Review.Biochemical genetics · 2025Review
- Bridging Nanomanufacturing and Artificial Intelligence-A Comprehensive Review.Materials (Basel, Switzerland) · 2024Review
- Machine Learning Enables Comprehensive Prediction of the Relative Protein Abundance of Multiple Proteins on the Protein Corona.Research (Washington, D.C.) · 2024Article
- Unveiling protein corona composition: predicting with resampling embedding and machine learning.Regenerative biomaterials · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Proteins encountered in biological and environmental systems bind to engineered nanomaterials (ENMs) to form a protein corona (PC) that alters the surface chemistry, reactivity, and fate of the ENMs. Complexities such as the diversity of the PC and variation with ENM properties and reaction conditions make the PC population difficult to predict. Here, we support the development of predictive models for PC populations by relating biophysicochemical characteristics of proteins, ENMs, and solution conditions to PC formation using random forest classification. The resulting model offers a predictive analysis into the population of PC proteins in Ag ENM systems of various ENM size and surface coatings. With an area under the receiver operating characteristic curve of 0.83 and F1-score of 0.81, a model with strong performance has been constructed based upon experimental data. The weighted contribution of each variable provides recommendations for mechanistic models based upon protein enrichment classification results. Protein biophysical properties such as pI and weight are weighted heavily. Yet, ENM size, surface charge, and solution ionic strength also proved essential to an accurate model. The model can be readily modified and applied to other ENM PC populations. The model presented here represents the first step toward robust predictions of PC fingerprints.
Identifiers
What OpenQuestion holds
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.