ReviewCell reports. Medicine2022
Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.
Review in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
36 citing papers in PubMed.
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Nasal Airway Transcriptome Reflects Selected Asthma-Associated Gene Signatures in the Lower Airways.Allergy · 2026Article
- Clinical applications of artificial intelligence-driven nitric oxide: a bibliometric and scientific mapping analysis.Medical gas research · 2026Review
- Mechanisms and therapeutic strategies of asthma: from bench to bedside.Signal transduction and targeted therapy · 2026Review
- Evolution, hotspots, and future directions of artificial intelligence in asthma research: a Web of Science-based bibliometric analysis [2016-2026].Journal of thoracic disease · 2026Article
- MicroRNA clustering in pediatric asthma reveals differential corticosteroid responsiveness.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2026Article
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Developments in the Management Strategies for Allergy: Advances in Artificial Intelligence and Future Perspectives.Anti-inflammatory & anti-allergy agents in medicinal chemistry · 2026Review
- Mepolizumab in Pediatric Severe Eosinophilic Asthma: A Review of Efficacy, Safety, and Clinical Applications.Journal of inflammation research · 2026Review
- Oxidative stress driven airway mesenchymal reprogramming in asthma: mechanistic insights and evidence from botanical drug formulations.Frontiers in pharmacology · 2026Review
- A principal component analysis-based endophenotype definition for change in lung function and inhaled corticosteroid treatment response in childhood asthma.Respiratory research · 2025Article
- IL-13 Modulates Antiviral Effector and Proinflammatory Pathways in Rhinovirus-Infected Pediatric Bronchial Epithelium.bioRxiv : the preprint server for biology · 2025Article
- Predictive model for inhaled corticosteroid response in hospitalized asthma: implications for precision medicine.Journal of thoracic disease · 2025Article
- Investigation of key ferroptosis-associated genes and potential therapeutic drugs for asthma based on machine learning and regression models.Scientific reports · 2025Article
- Advances in noninvasive imaging for detecting radiation-induced lung injury (RILI).International journal of radiation biology · 2025Review
- The role of mesenchymal stem cell‑derived exosomes in asthma (Review).Molecular medicine reports · 2025Review
- Article
- Unraveling the Molecular Landscape of Neutrophil Extracellular Traps in Severe Asthma: Identification of Biomarkers and Molecular Clusters.Molecular biotechnology · 2025Article
- Letter to the Editor Regarding "Predicting Asthma Exacerbations Using Machine Learning Models".Advances in therapy · 2025Article
- CD8+ TEMRAs in severe asthma associate with asthma symptom duration and escape proliferation arrest.JCI insight · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
There is unprecedented opportunity to use machine learning to integrate high-dimensional molecular data with clinical characteristics to accurately diagnose and manage disease. Asthma is a complex and heterogeneous disease and cannot be solely explained by an aberrant type 2 (T2) immune response. Available and emerging multi-omics datasets of asthma show dysregulation of different biological pathways including those linked to T2 mechanisms. While T2-directed biologics have been life changing for many patients, they have not proven effective for many others despite similar biomarker profiles. Thus, there is a great need to close this gap to understand asthma heterogeneity, which can be achieved by harnessing and integrating the rich multi-omics asthma datasets and the corresponding clinical data. This article presents a compendium of machine learning approaches that can be utilized to bridge the gap between predictive biomarkers and actual causal signatures that are validated in clinical trials to ultimately establish true asthma endotypes.
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.