Multiomic Investigations into Lung Health and Disease.

Area of Science:

• Pulmonary Medicine
• Genomics
• Biomedical Data Science

Background:

• Lung diseases cause over 5 million deaths globally, representing a significant healthcare burden.
• A comprehensive understanding of lung disease is crucial for improving patient outcomes, including mortality and quality of life.
• Multi-omics data integration offers a holistic approach to understanding complex lung diseases.

Purpose of the Study:

• To review the application of multi-omics investigations in lung disease research.
• To summarize multi-omics data integration strategies and machine learning applications in pulmonary research.
• To discuss various lung disease models used for multi-omics studies.

Main Methods:

• Literature review of multi-omics studies in lung diseases.
• Analysis of multi-omics data integration techniques.
• Examination of machine learning algorithms applied to lung multi-omics data.
• Review of animal models, organoids, and cell lines for lung multi-omics research.

Main Results:

• Multi-omics data integration provides deeper insights into the etiopathogenesis of lung diseases.
• These integrated datasets facilitate the classification of disease severity and prognostication.
• Multi-omics approaches aid in the discovery of novel biomarkers for lung conditions.
• Examples demonstrate improved preventative and therapeutic interventions based on multi-omics findings.

Conclusions:

• Multi-omics investigations are pivotal for advancing the understanding and management of lung diseases.
• The integration of multi-omics data, coupled with machine learning, holds significant promise for personalized lung medicine.
• Further research utilizing diverse lung disease models will continue to refine multi-omics applications.