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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

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.

Details

Title
Artificial Intelligence and Cardiovascular Genetics
Author
Krittanawong, Chayakrit 1 ; Johnson, Kipp W 2   VIAFID ORCID Logo  ; Choi, Edward 3 ; Kaplin, Scott 4   VIAFID ORCID Logo  ; Venner, Eric 5   VIAFID ORCID Logo  ; Murugan, Mullai 6 ; Wang, Zhen 7 ; Glicksberg, Benjamin S 2   VIAFID ORCID Logo  ; Amos, Christopher I 8   VIAFID ORCID Logo  ; Schatz, Michael C 9   VIAFID ORCID Logo  ; Wilson Tang, W H 10   VIAFID ORCID Logo 

 Section of Cardiology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Cardiovascular Medicine, NYU Langone, New York, NY 10016, USA; [email protected]; The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; [email protected] (K.W.J.); [email protected] (B.S.G.); Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; [email protected] 
 The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; [email protected] (K.W.J.); [email protected] (B.S.G.); Department of Genetics and Genomic Sciences, Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA 
 Google Health Research, Google, Mountain View, CA 94043, USA; [email protected] 
 Department of Cardiovascular Medicine, NYU Langone, New York, NY 10016, USA; [email protected] 
 Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; [email protected] 
 Human Genome Sequencing Center, Department of Software Development, Baylor College of Medicine, Houston, TX 77030, USA; [email protected] 
 Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN 55905, USA; [email protected]; Division of Health Care Policy and Research, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA 
 Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA; [email protected] 
 Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; [email protected]; Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA 
10  Department of Cardiovascular Medicine, Heart and Vascular Institute, Cleveland Clinic, Cleveland, OH 44195, USA; [email protected]; Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland, OH 44195, USA; Center for Clinical Genomics, Cleveland Clinic, Cleveland, OH 44195, USA 
First page
279
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20751729
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2633071506
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.