ArticleJournal of the American Medical Informatics Association : JAMIA2017
A genome-by-environment interaction classifier for precision medicine: personal transcriptome response to rhinovirus identifies children prone to asthma exacerbations.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Trial
- Paired-Sample and Pathway-Anchored MLOps Framework for Robust Transcriptomic Machine Learning in Small Cohorts: Model Classification Study.JMIR bioinformatics and biotechnology · 2025Article
- Article
- Gene-environment interactions in childhood asthma revisited; expanding the interaction concept.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2022Review
- Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS).NPJ digital medicine · 2021Article
- 'Single-subject studies'-derived analyses unveil altered biomechanisms between very small cohorts: implications for rare diseases.Bioinformatics (Oxford, England) · 2021Article
- Personalized beyond Precision: Designing Unbiased Gold Standards to Improve Single-Subject Studies of Personal Genome Dynamics from Gene Products.Journal of personalized medicine · 2020Article
- binomialRF: interpretable combinatoric efficiency of random forests to identify biomarker interactions.BMC bioinformatics · 2020Article
- Precision medicine in severe pediatric asthma: opportunities and challenges.Current opinion in pulmonary medicine · 2020Review
- Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine.BMC medical genomics · 2019Article
- Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes.Briefings in bioinformatics · 2019Review
- Development and validation of the PEPPER framework (Prenatal Exposure PubMed ParsER) with applications to food additives.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveTo introduce a disease prognosis framework enabled by a robust classification scheme derived from patient-specific transcriptomic response to stimulation. MATERIALS AND
methodsWithin an illustrative case study to predict asthma exacerbation, we designed a stimulation assay that reveals individualized transcriptomic response to human rhinovirus. Gene expression from peripheral blood mononuclear cells was quantified from 23 pediatric asthmatic patients and stimulated in vitro with human rhinovirus. Responses were obtained via the single-subject gene set testing methodology "N-of-1-pathways." The classifier was trained on a related independent training dataset (n = 19). Novel visualizations of personal transcriptomic responses are provided.
resultsOf the 23 pediatric asthmatic patients, 12 experienced recurrent exacerbations. Our classifier, using individualized responses and trained on an independent dataset, obtained 74% accuracy (area under the receiver operating curve of 71%; 2-sided P = .039). Conventional classifiers using messenger RNA (mRNA) expression within the viral-exposed samples were unsuccessful (all patients predicted to have recurrent exacerbations; accuracy of 52%). DISCUSSION: Prognosis based on single time point, static mRNA expression alone neglects the importance of dynamic genome-by-environment interplay in phenotypic presentation. Individualized transcriptomic response quantified at the pathway (gene sets) level reveals interpretable signals related to clinical outcomes.
conclusionThe proposed framework provides an innovative approach to precision medicine. We show that quantifying personal pathway-level transcriptomic response to a disease-relevant environmental challenge predicts disease progression. This genome-by-environment interaction assay offers a noninvasive opportunity to translate omics data to clinical practice by improving the ability to predict disease exacerbation and increasing the potential to produce more effective treatment decisions.
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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.