Taking Data Science to Heart: Next Scale of Gene Regulation

Purpose of Review Technical advances have facilitated high-throughput measurements of the genome in the context of cardiovascular biology. These techniques bring a deluge of gargantuan datasets, which in turn present two fundamentally new opportunities for innovation-data processing and knowledge integration-toward the goal of meaningful basic and translational discoveries. Recent Findings Big data, integrative analyses, and machine learning have brought cardiac investigations to the cutting edge of chromatin biology, not only to reveal basic principles of gene regulation in the heart, but also to aid in the design of targeted epigenetic therapies. Cardiac studies using big data are only beginning to integrate the millions of recorded data points and the tools of machine learning are aiding this process. Future experimental design should take into consideration insights from existing genomic datasets, thereby focusing on heretofore unexplored epigenomic contributions to disease pathology.

Automated summary

The advancements in technology have enabled high-throughput measurements of the genome in cardiovascular biology, leading to a flood of immense datasets that offer new opportunities for data processing and knowledge integration for meaningful discoveries. Big data, integrative analyses, and machine learning have brought cardiac investigations to the forefront of chromatin biology, with potential implications for targeted epigenetic therapies. Future research should consider insights from existing genomic datasets to explore uncharted epigenomic contributions to disease pathology.