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• W3136273732 abstract "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.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." @default.
• W3136273732 created "2021-03-29" @default.
• W3136273732 creator A5069588999 @default.
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• W3136273732 date "2021-03-15" @default.
• W3136273732 modified "2023-10-17" @default.
• W3136273732 title "Taking Data Science to Heart: Next Scale of Gene Regulation" @default.
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• W3136273732 doi "https://doi.org/10.1007/s11886-021-01467-6" @default.
• W3136273732 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8142693" @default.
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