ArticleJCI insight2024
Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.
Article in JCI insight, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Uncovering sodium overload-associated gene signatures in chronic obstructive pulmonary disease through integrated bioinformatics and machine learning.Journal of thoracic disease · 2026Article
- Aberrant mucin expression and keratinization distinguishing severe from mild asthma revealed by interpretable machine learning.JCI insight · 2026Article
- Benign vs. malignant pulmonary nodules: pleural adhesion risks and predictors.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Fueling the fire: metabolic dysfunction and senescence as drivers of lung aging and disease.Physiological reviews · 2026Review
- ANT1 Deficiency Impairs Macrophage Metabolism and Migration, Protecting Against Emphysema in Chronic Obstructive Pulmonary Disease.American journal of respiratory cell and molecular biology · 2025Article
- Femoral head diameter varies widely in hips with developmental dysplasia and predicts acetabular component size in total hip arthroplasty.Bone & joint open · 2025Article
- Interpretable machine learning coupled to spatial transcriptomics unveils mechanisms of macrophage-driven fibroblast activation in ischemic cardiomyopathy.medRxiv : the preprint server for health sciences · 2025Article
- Machine learning approaches enable the discovery of therapeutics across domains.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- An integrated machine learning model of transcriptomic genes in multi-center chronic obstructive pulmonary disease reveals the causal role of TIMP4 in airway epithelial cell.Respiratory research · 2025Article
- Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.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
14 authors.
Funding
Abstract
Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including chronic obstructive pulmonary disease (COPD). However, identifying relevant biologic causative factors has been limited by the integration of high dimensionality data. COPD is characterized by lung destruction and inflammation, with smoke exposure being a major risk factor. To define previously unknown biological mechanisms in COPD, we utilized unsupervised and supervised interpretable machine learning analyses of single-cell RNA-Seq data from the mouse smoke-exposure model to identify significant latent factors (context-specific coexpression modules) impacting pathophysiology. The machine learning transcriptomic signatures coupled to protein networks uncovered a reduction in network complexity and new biological alterations in actin-associated gelsolin (GSN), which was transcriptionally linked to disease state. GSN was altered in airway epithelial cells in the mouse model and in human COPD. GSN was increased in plasma from patients with COPD, and smoke exposure resulted in enhanced GSN release from airway cells from patients with COPD. This method provides insights into rewiring of transcriptional networks that are associated with COPD pathogenesis and provides a translational analytical platform for other diseases.
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.