Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

Area of Science:

• Cardiovascular research
• Computational biology
• Precision medicine

Background:

• Cardiovascular diseases (CVDs) present complex, heterogeneous phenotypes, challenging treatment development.
• Diverse molecular mechanisms underlie CVDs, necessitating advanced analytical approaches.
• Precision medicine requires identifying distinct patient subgroups for tailored therapies.

Purpose of the Study:

• To review computational approaches for subtyping cardiovascular diseases.
• To outline essential components for selecting, integrating, and clustering omics and clinical data.
• To discuss challenges and future directions in CVD subtyping for clinical application.

Main Methods:

• Review of computational strategies for data selection, integration, and clustering.
• Analysis of feature selection, extraction, and algorithm application in CVD research.
• Examination of subtyping pipelines applied to heart failure and coronary artery disease.

Main Results:

• Computational subtyping utilizes phenotypic and multiomics data to identify distinct CVD patient subgroups.
• Key challenges exist in feature selection, data integration, and clustering algorithm implementation.
• Successful subtyping pipelines have been developed for heart failure and coronary artery disease.

Conclusions:

• Robust computational subtyping is crucial for advancing precision medicine in cardiovascular care.
• Integration of omics and clinical data enables identification of unique disease pathogeneses.
• Future work should focus on translating subtyping approaches into clinical workflows for improved patient outcomes.