ArticleScientific reports2018
Identification of Novel Genes in Human Airway Epithelial Cells associated with Chronic Obstructive Pulmonary Disease (COPD) using Machine-Based Learning Algorithms.
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 24 papers.
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24 citing papers in PubMed, 43 citations in OpenAlex.
- Transcriptomic landscape of airway epithelial repair: Contrasting acute and chronic injury in mustard lung and COPD.Journal, genetic engineering & biotechnology · 2026Article
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- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Airway MMP-12 and DNA methylation in COPD: an integrative approach.Respiratory research · 2025Article
- Machine learning-based estimation of patient body weight from radiation dose metrics in computed tomography.Journal of applied clinical medical physics · 2024Article
- Identifying plasma proteomic signatures from health to heart failure, across the ejection fraction spectrum.Scientific reports · 2024Article
- Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture.Genes & diseases · 2024Article
- Machine Learning Reveals Impacts of Smoking on Gene Profiles of Different Cell Types in Lung.Life (Basel, Switzerland) · 2024Article
- Inefficient antiviral response in reconstituted small-airway epithelium from chronic obstructive pulmonary disease patients following human parainfluenza virus type 3 infection.Virology journal · 2024Article
- Machine Learning-Based Proteomics Reveals Ferroptosis in COPD Patient-Derived Airway Epithelial Cells Upon Smoking Exposure.Journal of Korean medical science · 2023Article
- Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.Scientific reports · 2023Article
- Machine learning algorithms for identifying predictive variables of mortality risk following dementia diagnosis: a longitudinal cohort study.Scientific reports · 2023Article
- The role of macrophage subtypes and exosomes in immunomodulation.Cellular & molecular biology letters · 2022Review
- Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.BMC cardiovascular disorders · 2022Article
- Identification ofBiochemistry and biophysics reports · 2021Article
- Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach.Saudi journal of biological sciences · 2021Article
- Novel computational analysis of large transcriptome datasets identifies sets of genes distinguishing chronic obstructive pulmonary disease from healthy lung samples.Scientific reports · 2021Article
- Strain-specific behavior of Mycobacterium tuberculosis in A549 lung cancer cell line.BMC bioinformatics · 2021Article
- Intellectual disability: dendritic anomalies and emerging genetic perspectives.Acta neuropathologica · 2021Review
- Reticular Basement Membrane Thickness Is Associated with Growth- and Fibrosis-Promoting Airway Transcriptome Profile-Study in Asthma Patients.International journal of molecular sciences · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this project was to identify candidate novel therapeutic targets to facilitate the treatment of COPD using machine-based learning (ML) algorithms and penalized regression models. In this study, 59 healthy smokers, 53 healthy non-smokers and 21 COPD smokers (9 GOLD stage I and 12 GOLD stage II) were included (n = 133). 20,097 probes were generated from a small airway epithelium (SAE) microarray dataset obtained from these subjects previously. Subsequently, the association between gene expression levels and smoking and COPD, respectively, was assessed using: AdaBoost Classification Trees, Decision Tree, Gradient Boosting Machines, Naive Bayes, Neural Network, Random Forest, Support Vector Machine and adaptive LASSO, Elastic-Net, and Ridge logistic regression analyses. Using this methodology, we identified 44 candidate genes, 27 of these genes had been previously been reported as important factors in the pathogenesis of COPD or regulation of lung function. Here, we also identified 17 genes, which have not been previously identified to be associated with the pathogenesis of COPD or the regulation of lung function. The most significantly regulated of these genes included: PRKAR2B, GAD1, LINC00930 and SLITRK6. These novel genes may provide the basis for the future development of novel therapeutics in COPD and its associated morbidities.
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