Artificial Intelligence and Cardiovascular Genetics

被引：30

作者：

Krittanawong, Chayakrit ^{[1
,2
,3
,4
]}

Johnson, Kipp W. ^{[3
,5
]}

Choi, Edward ^{[6
]}

Kaplin, Scott ^{[2
]}

Venner, Eric ^{[4
]}

Murugan, Mullai ^{[7
]}

Wang, Zhen ^{[8
,9
]}

Glicksberg, Benjamin S. ^{[3
,5
]}

Amos, Christopher I. ^{[10
]}

Schatz, Michael C. ^{[11
,12
]}

Tang, W. H. Wilson ^{[13
,14
,15
]}

机构：

[1] Baylor Coll Med, Sect Cardiol, Houston, TX 77030 USA

[2] NYU Langone, Dept Cardiovasc Med, New York, NY 10016 USA

[3] Icahn Sch Med Mt Sinai, Hasso Plattner Inst Digital Hlth Mt Sinai, New York, NY 10029 USA

[4] Baylor Coll Med, Human Genome Sequencing Ctr, Dept Mol & Human Genet, Houston, TX 77030 USA

[5] Icahn Sch Med Mt Sinai, Inst Next Generat Healthcare, Dept Genet & Genom Sci, New York, NY 10029 USA

[6] Google, Google Hlth Res, Mountain View, CA 94043 USA

[7] Baylor Coll Med, Human Genome Sequencing Ctr, Dept Software Dev, Houston, TX 77030 USA

[8] Mayo Clin, Robert D & Patricia E Kern Ctr Sci Hlth Care Deli, Rochester, MN 55905 USA

[9] Mayo Clin, Dept Hlth Sci Res, Div Hlth Care Policy & Res, Rochester, MN 55905 USA

[10] Baylor Coll Med, Dan L Duncan Comprehens Canc Ctr, Houston, TX 77030 USA

[11] Johns Hopkins Univ, Dept Comp Sci, Baltimore, MD 21218 USA

[12] Johns Hopkins Univ, Dept Biol, Baltimore, MD 21218 USA

[13] Cleveland Clin, Inst Heart & Vasc, Dept Cardiovasc Med, Cleveland, OH 44195 USA

[14] Lerner Res Inst, Dept Cellular & Mol Med, Cleveland, OH 44195 USA

[15] Cleveland Clin, Ctr Clin Genom, Cleveland, OH 44195 USA

来源：

LIFE-BASEL | 2022年 / 12卷 / 02期

关键词：

genomics; AI; genetics; deep learning; cardiovascular disease; cardiology; machine learning; artificial intelligence; CORONARY-ARTERY-DISEASE; POLYGENIC RISK SCORES; HYPERTROPHIC CARDIOMYOPATHY; PREDICTION; GENOMICS; ASSOCIATION; VARIANTS; IDENTIFICATION; VALIDATION; HEALTH;

D O I：

10.3390/life12020279

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：28

共 173 条

[1] BioVEC: A program for Biomolecule Visualization with Ellipsoidal Coarse-graining [J].

Abrahamsson, Erik ;

Plotkin, Steven S. .

JOURNAL OF MOLECULAR GRAPHICS & MODELLING, 2009, 28 (02) :140-145

[2] Establishment of Specialized Clinical Cardiovascular Genetics Programs: Recognizing the Need and Meeting Standards: A Scientific Statement From the American Heart Association [J].

Ahmad, Ferhaan ;

McNally, Elizabeth M. ;

Ackerman, Michael J. ;

Baty, Linda C. ;

Day, Sharlene M. ;

Kullo, Iftikhar J. ;

Madueme, Peace C. ;

Maron, Martin S. ;

Martinez, Matthew W. ;

Salberg, Lisa ;

Taylor, Matthew R. ;

Wilcox, Janel E. .

CIRCULATION-GENOMIC AND PRECISION MEDICINE, 2019, 12 (06) :286-305

[3] A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data [J].

Ainscough, Benjamin J. ;

Barnell, Erica K. ;

Ronning, Peter ;

Campbell, Katie M. ;

Wagner, Alex H. ;

Fehniger, Todd A. ;

Dunn, Gavin P. ;

Uppaluri, Ravindra ;

Govindan, Ramaswamy ;

Rohan, Thomas E. ;

Griffith, Malachi ;

Mardis, Elaine R. ;

Swamidass, S. Joshua ;

Griffith, Obi L. .

NATURE GENETICS, 2018, 50 (12) :1735-+

[4]

Al-Khatib SM, 2018, CIRCULATION, V138, pE210, DOI [10.1161/CIR.0000000000000548, 10.1161/CIR.0000000000000549]

[5] Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants [J].

Alaa, Ahmed M. ;

Bolton, Thomas ;

Di Angelantonio, Emanuele ;

Rudd, James H. F. ;

van der Schaar, Mihaela .

PLOS ONE, 2019, 14 (05)

[6] Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning [J].

Alipanahi, Babak ;

Delong, Andrew ;

Weirauch, Matthew T. ;

Frey, Brendan J. .

NATURE BIOTECHNOLOGY, 2015, 33 (08) :831-+

[7]

Amarbayasgalan Tsatsral, 2020, Advances in Intelligent Information Hiding and Multimedia Signal Processing. Proceedings of the 15th International Conference on IIH-MSP in conjunction with the 12th International Conference on FITAT. Smart Innovation, Systems and Technologies (SIST 156), P273, DOI 10.1007/978-981-13-9714-1_30

[8] Reconstruction error based deep neural networks for coronary heart disease risk prediction [J].