ArticleFrontiers in physiology2021
Utilizing the Heterogeneity of Clinical Data for Model Refinement and Rule Discovery Through the Application of Genetic Algorithms to Calibrate a High-Dimensional Agent-Based Model of Systemic Inflammation.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed, 27 citations in OpenAlex.
- Digital twins to accelerate target identification and drug development for immune-mediated disorders.FEBS open bio · 2026Review
- Experimentally calibrated multiscale model predicts schedule dependent drug combination effects.NPJ systems biology and applications · 2026Article
- Machine learning for extracellular vesicles enables diagnostic and therapeutic nanobiotechnology.Journal of nanobiotechnology · 2026Review
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- Hybrid modelling using simulation and machine learning in healthcare.Computers & operations research · 2026Review
- Article
- The Wound Environment Agent-based Model (WEABM): a digital twin platform for characterization and complex therapeutic discovery for volumetric muscle loss.bioRxiv : the preprint server for biology · 2024Article
- Creating a Roadmap to Quantitative Systems Pharmacology-Informed Rare Disease Drug Development: A Workshop Report.Clinical pharmacology and therapeutics · 2024Review
- Characterizing the Crosstalk Between Programmed Cell Death Pathways in Cytokine Storm With an Agent-Based Model.Surgical infections · 2023Article
- Generating synthetic multidimensional molecular time series data for machine learning: considerations.Frontiers in systems biology · 2023Article
- Real World Data Studies of Antineoplastic Drugs: How Can They Be Improved to Steer Everyday Use in the Clinic?Pragmatic and observational research · 2023Review
- Preparing for the next COVID: Deep Reinforcement Learning trained Artificial Intelligence discovery of multi-modal immunomodulatory control of systemic inflammation in the absence of effective anti-microbials.bioRxiv : the preprint server for biology · 2022Article
- Preparing for the next pandemic: Simulation-based deep reinforcement learning to discover and test multimodal control of systemic inflammation using repurposed immunomodulatory agents.Frontiers in immunology · 2022Article
- Drug Development Digital Twins for Drug Discovery, Testing and Repurposing: A Schema for Requirements and Development.Frontiers in systems biology · 2022Article
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- Article
- The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis.Frontiers in physiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
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.