ArticleScientific reports2018
A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.
Article in Scientific reports, 2018. 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, 68 citations in OpenAlex.
- Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence.BMC medical research methodology · 2025Pooled it
- Observational
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- Machine Learning Analysis of the Orbitofrontal Cortex Transcriptome of Human Opioid Users Identifies Shisa7 as a Translational Target Relevant for Heroin Seeking Leveraging a Male Rat Model.Biological psychiatry · 2025Article
- Systems immunology: When systems biology meets immunology.Frontiers in immunology · 2025Review
- M2 macrophage derived HMOX1 defines chronic rhinosinusitis with nasal polyps.Clinical and translational allergy · 2024Article
- AI and immunology as a new research paradigm.Nature immunology · 2024Article
- CCDC113 promotes colorectal cancer tumorigenesis and metastasis via TGF-β signaling pathway.Cell death & disease · 2024Article
- Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.The Journal of allergy and clinical immunology · 2024Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma.Clinical and translational allergy · 2023Article
- On the challenges of predicting treatment response in Hodgkin's Lymphoma using transcriptomic data.BMC medical genomics · 2023Article
- Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.Scientific reports · 2023Article
- Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.Cell reports. Medicine · 2022Review
- Asthma exacerbations: the Achilles heel of asthma care.Trends in molecular medicine · 2022Review
- Peripheral blood transcriptomic clusters uncovered immune phenotypes of asthma.Respiratory research · 2022Article
- Accurate diagnosis of atopic dermatitis by combining transcriptome and microbiota data with supervised machine learning.Scientific reports · 2022Article
- Potential Metabolic Biomarkers in Adult Asthmatics.Metabolites · 2021Review
- Predicting youth diabetes risk using NHANES data and machine learning.Scientific reports · 2021Article
- Transcriptome-wide and differential expression network analyses of childhood asthma in nasal epithelium.The Journal of allergy and clinical immunology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.
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.