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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Artificial Intelligence and Cardiovascular Genetics.

Chayakrit Krittanawong^1,2,3,4, Kipp W Johnson^3,5, Edward Choi⁶

¹Section of Cardiology, Baylor College of Medicine, Houston, TX 77030, USA.

Life (Basel, Switzerland)

|February 25, 2022

Summary

This summary is machine-generated.

Artificial intelligence (AI) and next-generation sequencing (NGS) offer new ways to understand complex polygenic cardiovascular diseases (CVDs). This integration promises deeper insights, improved prognostics, and personalized medicine for patients.

Keywords:

AI artificial intelligence cardiology cardiovascular disease deep learning genetics genomics machine learning

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Published on: January 16, 2019

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Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

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Published on: September 26, 2018

Area of Science:

Genomics
Cardiovascular Medicine
Artificial Intelligence

Background:

Polygenic diseases result from multiple genes, complicating diagnosis and management.
Clinical heterogeneity in polygenic cardiovascular diseases (CVDs) is a significant challenge in cardiovascular medicine.
Understanding genetic contributions to CVDs is a major research goal.

Purpose of the Study:

To review the opportunities and limitations of genomics in understanding polygenic CVDs.
To provide an overview of artificial intelligence (AI) and its role in genomic analysis.
To identify current and future applications of AI in cardiovascular genomics.

Main Methods:

Leveraging advances in artificial intelligence (AI) and next-generation sequencing (NGS) technologies.
Analyzing complex biological genomic data for insights into polygenic diseases.
Integrating genomic data with clinical information, including imaging and biomarkers.

Main Results:

AI and NGS provide unprecedented capabilities for dynamic and complex genomic analyses.
Combining these technologies can lead to a deeper understanding of heterogeneous polygenic CVDs.
Potential for improved prognostic guidance and personalized medicine.

Conclusions:

Genomic characterization and data integration are key to advancing polygenic CVD research.
AI holds significant promise for unlocking complex genomic patterns in cardiovascular disease.
Future directions involve robust genomic characterization and AI-driven integration for personalized cardiovascular care.