About this Research Topic
Recent studies have shown great promise in the application of artificial intelligence (AI) to personalized medicine approaches in cardiovascular medicine, particularly machine learning (ML). ML analysis of multi-modal datasets may permit personalized cardiovascular medicine approaches, leading to a greater understanding of cardiovascular health and disease.
Additionally, the application of novel technologies from molecular biology such as organs-on-a-chip or organoids, to computational science such as digital twins, may aid in the development of personalized cardiovascular treatments. Organs-on-a-chip and organoids could be engineered to mimic an individuals’ cardiovascular physiology, providing new opportunities for personalized assessment of drug efficacy and treatment strategies. Whereas, digital twins, combining data, knowledge, and AI algorithms may be used as tool to allow computational analysis of potential personalized cardiovascular medicine approaches.
This Research Topic was developed with the aim of highlighting recent developments in research in two subject areas:
1) Systems biology, including network analysis and machine learning models to analyse complex multi-omics interactions to support the decision process in clinical cardiovascular management.
2) Novel bioengineered tools to model and personalize new cardiovascular treatments.
Keywords: Cardiovascular Disease, Bioinformatics, Personalized Medicine, Machine learning, Organs-on-a-chip, Digital Twins, Artificial Intelligence
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