{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sui J"],"funding":["National Institute of Allergy and Infectious Diseases","University of Pittsburgh, Institute for Clinical Research and Education TL1 Fellowship","LongFonds BREATH Consortium","NIA NIH HHS","national institute of aging","NIAID NIH HHS","NHLBI NIH HHS","National Heart, Lung, and Blood Institute","Burroughs Wellcome Fund - Career Award for Medical Scientists","Parker B Francis Pulmonary Fellowship","UPMC Immune Transplant and Therapy Center"],"pagination":["e180239"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11601586"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(21)"],"pubmed_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 comple"],"journal":["JCI insight"],"pubmed_title":["Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD."],"pmcid":["PMC11601586"],"funding_grant_id":["n/a","DP2 AI164325","U54 AG075931","K08HL141595","DP2AI164325","F30HL165827","R01 HL141380","R01 HL168050","R01HL141380","R01HL168050","ITTC grant","U54AG075931","F30 HL165827","K08 HL141595"],"pubmed_authors":["Konigshoff M","Gregory AD","Kapellos TS","Kliment CR","Mbaekwe U","Das J","Ting NC","Sciurba F","Murday K","Zhang Y","Xiao H","Sui J","Hu Q","Yildirim AO"],"additional_accession":[]},"is_claimable":false,"name":"Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.","description":"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 comple","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-05-04T06:48:45.214Z","creation":"2025-04-04T02:33:31.705Z"},"accession":"S-EPMC11601586","cross_references":{"pubmed":["39352744"],"doi":["10.1172/jci.insight.180239"]}}