Integrative Multi-Omics and Bioinformatics Approaches in Cardiovascular Disease

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, driven by complex and heterogeneous molecular mechanisms involving genetic, epigenetic, transcriptomic, proteomic, and metabolic alterations. Traditional single-layer analyses often fail to capture this biological complexity, limiting our understanding of disease mechanisms and the identification of robust biomarkers or therapeutic targets. In recent years, advances in high-throughput technologies and bioinformatics have enabled the generation and integration of large-scale multi-omics datasets, offering unprecedented opportunities to dissect cardiovascular pathophysiology at a systems level.Integrative multi-omics approaches combining transcriptomics, proteomics, epigenomics, metabolomics, and single-cell technologies have become essential tools to unravel molecular networks underlying cardiovascular remodeling, inflammation, fibrosis, atherosclerosis, heart failure, and vascular dysfunction. Furthermore, the increasing availability of public datasets and the application of artificial intelligence (AI) and machine learning methods have accelerated data-driven discoveries and improved predictive modeling in cardiovascular research.This Research Topic aims to provide a comprehensive platform for original research articles, reviews, and methodological studies that leverage bioinformatics, computational biology, and multi-omics integration to advance our understanding of cardiovascular diseases. We welcome studies that move beyond descriptive analyses and emphasize biological interpretation, mechanistic insights, and translational relevance.Topics of interest include, but are not limited to:• Integrative analysis of transcriptomic, proteomic, metabolomic, and epigenomic data in cardiovascular diseases• Single-cell and spatial omics approaches to characterize cellular heterogeneity in cardiac and vascular tissues• Bioinformatics-driven identification of molecular signatures, biomarkers, and therapeutic targets• Systems biology and network-based analyses of cardiovascular remodeling, inflammation, and fibrosis• Reanalysis and meta-analysis of public cardiovascular omics datasets• Machine learning and artificial intelligence applications for risk prediction, patient stratification, and outcome modeling• Computational approaches linking omics data with clinical, imaging, or histopathological features• Experimental validation of bioinformatics findings in preclinical or clinical settingsBy integrating computational and experimental perspectives, this Research Topic seeks to bridge the gap between large-scale data generation and clinically meaningful insights. The collection will highlight how integrative bioinformatics approaches can enhance reproducibility, improve mechanistic understanding, and support precision medicine strategies in cardiovascular disease. This Topic will be of interest to basic scientists, bioinformaticians, translational researchers, and clinicians working at the interface of data science and cardiovascular medicine. Topic editors: Juan Manuel Velázquez-Enríquez, Rafael Baltierrez-Hoyos Source: https://www.frontiersin.org/research-topics/78801/integrative-multi-omics-and-bioinformatics-approaches-in-cardiovascular-disease