Artificial intelligence in multi-omics analysis of heart diseases

Cardiovascular diseases (CVDs) are a group of complex and diverse conditions and represent a major global public health burden. There is a need for better methods for early diagnosis and stratification. The application of artificial intelligence (AI) in multi-omics analysis is enhancing cardiovascular research by integrating genomic, transcriptomic, proteomic, metabolomic, and other multi-omic profiles to discover important molecular markers across different disease subtypes. Machine Learning and Explainable AI techniques are currently employed to enhance risk forecasting of early cardiometabolic conditions and to accurately classify CVDs. These models examine extensive intricate data from biological origins, discover new biomarkers, categorize disease subtypes, and tailor patient treatment. This chapter explores the core AI driven frameworks transforming the CVD research, including deep learning methods, ensemble and hybrid models, explainable AI techniques, integrative multi-omics algorithms, and advanced network analytics.