Multi-omics subtyping pipeline for chronic obstructive pulmonary disease

• Published in: PloS one Vol. 16; no. 8; p. e0255337
• Main Authors: Gillenwater, Lucas A., Helmi, Shahab, Stene, Evan, Pratte, Katherine A., Zhuang, Yonghua, Schuyler, Ronald P., Lange, Leslie, Castaldi, Peter J., Hersh, Craig P., Banaei-Kashani, Farnoush, Bowler, Russell P., Kechris, Katerina J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 25.08.2021; Public Library of Science (PLoS)
• Subjects: Age Factors; Aged; Biology and Life Sciences; Blood tests; Carbon monoxide; Care and treatment; Chronic obstructive pulmonary disease; Clinical medicine; Cluster Analysis; Clustering; College campuses; Computer science; Databases, Factual; Datasets; Diagnosis; Engineering schools; Female; Fibrillation; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation; Gene Regulatory Networks; Health risk assessment; Heterogeneity; Humans; Immune response; Informatics; Lung diseases; Lung diseases, Obstructive; Male; Medicine; Medicine and Health Sciences; Metabolites; Metabolomics; Metabolomics - methods; Methods; Middle Aged; Molecular biology; Obstructive lung disease; Phenotype; Phenotypes; Proteins; Proteomes; Proteomics; Proteomics - methods; Public health; Pulmonary Disease, Chronic Obstructive - blood; Pulmonary Disease, Chronic Obstructive - classification; Pulmonary Disease, Chronic Obstructive - genetics; Research and Analysis Methods; Risk factors; Sample size; Sphingomyelin; Support Vector Machine; Support vector machines; Transcriptomes; United States; United States > US; Colorado

Chronic Obstructive Pulmonary Disease (COPD) is the third leading cause of mortality in the United States; however, COPD has heterogeneous clinical phenotypes. This is the first large scale attempt which uses transcriptomics, proteomics, and metabolomics (multi-omics) to determine whether there are...