About this Research Topic
Systems biology is revolutionising the field of healthcare, with systems biology approaches that integrate comprehensive individual multi-omics data with clinical data from Electronic Health Records forming the basis of personalized medicine. However, computational models are required to analyse these data to support any potential personalized medical decisions. The development of advanced high-throughput screening technologies and assays, including genome sequencing, -omics studies, and novel imaging techniques have created a need for novel strategies to analyse and interpret large amounts of data. As personalized medicine approaches based on mono-modal data are often inaccurate and do not consider the whole picture, a multi-modal data approach is required.
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.
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
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
