Major pathophysiological changes in pulmonary disease provided a molecular insight based on deep learning approach.

Area of Science:

• Pulmonary Medicine
• Bioinformatics
• Artificial Intelligence

Background:

• Pulmonary disorders pose significant health challenges, often exacerbated by delayed diagnosis and treatment.
• Traditional methods struggle with complex airway diseases due to incomplete pathophysiological and molecular understanding.
• Existing research lacks comprehensive data on molecular pathways and signature molecules, limiting therapeutic options like nitric oxide (NO) and hydrogen sulfide (H2S).

Purpose of the Study:

• To leverage deep machine learning and network analysis to elucidate molecular pathways in major pulmonary diseases.
• To identify critical molecular players and their interconnections in airway disease progression.
• To provide a foundation for novel therapeutic interventions targeting identified pathways.

Main Methods:

• A deep machine learning approach utilizing AI as a search engine to analyze scientific studies and databases.
• MEDLINE search for published studies on major pulmonary diseases.
• NIH database utilized for gene and protein information.
• ShinyGo tool employed for pathway and molecular analysis.
• Bioinformatics analysis of gene expression profiles and protein-protein interactions using the STRING network.

Main Results:

• Identification of key molecular pathways correlated with pulmonary diseases.
• Demonstration of the interplay between perturbed molecules in airway disease progression.
• Network analysis revealed significant molecular interconnections relevant to disease pathology.

Conclusions:

• Perturbed molecules play a crucial role in the progression of airway diseases.
• Targeting identified molecular pathways presents a potential therapeutic strategy for pulmonary disorders.
• This AI-driven approach enhances understanding of molecular mechanisms for improved disease management.