Identification of Novel Genes in Human Airway Epithelial Cells associated with Chronic Obstructive Pulmonary Disease (COPD) using Machine-Based Learning Algorithms

• Published in: Scientific reports Vol. 8; no. 1; pp. 15775 - 20
• Main Authors: Mostafaei, Shayan, Kazemnejad, Anoshirvan, Azimzadeh Jamalkandi, Sadegh, Amirhashchi, Soroush, Donnelly, Seamas C., Armstrong, Michelle E., Doroudian, Mohammad
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 25.10.2018; Nature Publishing Group
• Subjects: 119/118; 631/114/1305; 631/114/2397; 631/114/2407; 631/114/2415; Algorithms; Bayesian analysis; Chronic obstructive pulmonary disease; DNA microarrays; Drug development; Epithelial cells; Epithelium; Gene expression; Gold; Humanities and Social Sciences; Learning algorithms; Lung diseases; multidisciplinary; Neural networks; Obstructive lung disease; Pathogenesis; Regression analysis; Respiratory function; Respiratory tract; Science; Science (multidisciplinary); Smoking; Therapeutic applications; ML Algorithms; Glutamic Acid Decarboxylase (GAD1); Small Airway Epithelium (SAE); Healthy Smokers; Chronic Obstructive Pulmonary Disease (COPD)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-018-33986-8

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.