ArticleScience advances2022
Supervised learning model predicts protein adsorption to carbon nanotubes.
Article in Science advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 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
- Surface complexation and multilayer formation in the adsorption of NADA and phosphate on magnetic iron oxide nanoparticles: implications for bioseparation.Nanoscale advances · 2026Article
- Revisiting Colloid Theory for Biomedicine: Reconciling Simple Models in Complex Environments.ACS nano medicine · 2026Article
- New Frontiers in AI-Nano Converged Platforms for Intelligent Diagnostics, Therapeutics, and Safety Evaluation.Chem & bio engineering · 2026Review
- Leveraging nanoparticle protein corona to advance plasma proteome profiling.Nature communications · 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
- AI and organoid platforms for brain-targeted theranostics.Theranostics · 2026Review
- Decoy Nanoparticles and Protein Corona Modulation: A Novel Frontier in Sepsis Treatment.International journal of nanomedicine · 2026Review
- In Silico Modeling of Nanoparticle Transport across the Blood-Brain Barrier: A Systematic Review.Computational and structural biotechnology journal · 2026Review
- Machine Learning Prediction of Protein Adsorption on Drug-delivering Nanoparticles: A Literature Survey and Need for Future Development.Pharmaceutical research · 2026Review
- Single-walled carbon nanotubes sensors: Preparation and bio-application advances.Pharmaceutical science advances · 2025Review
- Nanocorona-engineered gold nanostars orchestrate photothermal augmented immunotherapy and durable cancer vaccination.Journal of nanobiotechnology · 2025Article
- Discovery of Surface-Engineered Nanoparticles That Boost Enzyme Activity via High-Throughput Screening and Machine Learning.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- 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
- Single-Walled Carbon Nanotube Probes for Protease Characterization Directly in Cell-Free Expression Reactions.Analytical chemistry · 2025Article
- Beyond Correlation: Establishing Causality in Protein Corona Formation for Nanomedicine.Molecular pharmaceutics · 2025Article
- Conjugation Strategies for Low Solubility Proteins to Single-Walled Carbon Nanotubes as a Sensitive Fluorescent Assay to Protease Activity.Advanced materials interfaces · 2025Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Engineered nanoparticles are advantageous for biotechnology applications including biomolecular sensing and delivery. However, testing compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable protein corona formation prevents their effective implementation. We develop a random forest classifier trained with mass spectrometry data to identify proteins that adsorb to nanoparticles based solely on the protein sequence (78% accuracy, 70% precision). We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)–based nanosensor and study the relationship between the protein’s amino acid–based properties and binding capacity. Protein features associated with increased likelihood of SWCNT binding include high content of solvent-exposed glycines and nonsecondary structure–associated amino acids. To evaluate its predictive power, we apply the classifier to identify proteins with high binding affinity to SWCNTs, with experimental validation. The developed classifier provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions.
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.