Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.

Journal: Frontiers in endocrinology

PMID: 39845878

Abstract

BACKGROUND: Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on affected individuals, healthcare systems, and society. However, the specific molecular mechanisms supporting their interrelationship have not been fully defined.

Authors

Qianqian Liang

Department of Integrated Pulmonology, Fourth Clinical Medical College of Xinjiang Medical University, Urumqi, Xinjiang, China.
Yide Wang

Department of Integrated Pulmonology, Fourth Clinical Medical College of Xinjiang Medical University, Urumqi, Xinjiang, China.
Zheng Li

Department of Integrated Pulmonology, Fourth Clinical Medical College of Xinjiang Medical University, Urumqi, Xinjiang, China.

Keywords

Animals Biomarkers Computational Biology Diabetes Mellitus Gene Expression Profiling Gene Regulatory Networks Humans Machine Learning Mice Multiomics Pulmonary Disease, Chronic Obstructive

External Resources

View on PubMed Access via DOI PubMed (39845878)

Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.

Abstract

Authors

Keywords

External Resources

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