Artificial Intelligence and Cardiovascular Genetics

Krittanawong, Chayakrit, Johnson, Kipp W, Choi, Edward, Kaplin, Scott, Venner, Eric, Murugan, Mullai, Wang, Zhen, Glicksberg, Benjamin S, Amos, Christopher I, Schatz, Michael C, Tang, WH Wilson (February 2022) Artificial Intelligence and Cardiovascular Genetics. Life, 12 (2). p. 279. ISSN 2075-1729

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Abstract

<jats:p>Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.</jats:p>

Item Type:	Paper
Subjects:	diseases & disorders > cardiovascular diseases bioinformatics > genomics and proteomics > genetics & nucleic acid processing > population genetics
CSHL Authors:	Schatz, Michael C.
Communities:	CSHL labs > Schatz lab
SWORD Depositor:	CSHL Elements
Depositing User:	CSHL Elements
Date:	14 February 2022
Date Deposited:	07 Mar 2022 21:16
Last Modified:	15 Nov 2023 18:53
PMCID:	PMC8875522
URI:	https://repository.cshl.edu/id/eprint/40540

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