Artificial intelligence in multi-omics analysis of heart diseases

Area of Science:

• Cardiovascular research
• Artificial intelligence applications
• Multi-omics data integration

Background:

• Cardiovascular diseases (CVDs) pose a significant global health challenge.
• Current diagnostic and stratification methods for CVDs require improvement.
• There is a growing need for advanced analytical approaches in cardiovascular research.

Purpose of the Study:

• To explore artificial intelligence (AI) driven frameworks transforming cardiovascular disease (CVD) research.
• To highlight the integration of multi-omics data for enhanced CVD analysis.
• To discuss AI's role in early diagnosis, risk stratification, and personalized treatment of CVDs.

Main Methods:

• Application of artificial intelligence (AI) in multi-omics analysis (genomic, transcriptomic, proteomic, metabolomic).
• Utilizing Machine Learning and Explainable AI (XAI) for risk forecasting and disease classification.
• Exploring deep learning, ensemble models, hybrid models, and network analytics.

Main Results:

• AI enhances the discovery of molecular markers across diverse CVD subtypes.
• Machine Learning and XAI improve early cardiometabolic risk forecasting and CVD classification.
• AI models analyze complex biological data to identify new biomarkers and tailor patient treatments.

Conclusions:

• AI-driven multi-omics analysis is revolutionizing cardiovascular research.
• AI facilitates more accurate early diagnosis, risk stratification, and personalized treatment strategies for CVDs.
• The integration of advanced AI techniques holds significant promise for advancing CVD management.