Artificial Intelligence in Digital Genome Market to Cross US$ 3,601.7 Million with a CAGR of 44.8% by 2030 | NVIDIA Corp

BURLINGAME, CA, UNITED STATES, June 9, 2023/EINPresswire.com/ -- The global Artificial Intelligence in Digital Genome Market market is projected to surpass US$ 3,601.7 million by the end of 2030, in terms of revenue, growing at a CAGR of 44.8% during the forecast period (2023 to 2030). Artificial intelligence (AI) plays a significant role in the field of digital genomics, offering powerful tools and techniques to analyze and interpret the vast amount of genomic data generated through high-throughput sequencing technologies. The integration of AI and digital genomics holds great promise for advancing our understanding of genetic information, improving disease diagnosis and treatment, and facilitating precision medicine. However, it is important to ensure the ethical use of AI, address potential biases, and maintain data privacy and security when dealing with sensitive genomic information.

CMI has recently released the Artificial Intelligence in Digital Genome Market Market study, which includes over 100+ market data tables, pie charts, and figures. The research assessment aims to analyze future trends, growth factors, industry opinions, and industry-validated market facts to provide a forecast until 2030. The market study is segmented by key regions that are driving market growth. This section also outlines the scope of different segments and applications that have the potential to influence the market in the future.

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**Note – Updated Version 2023 is available

(The sample of this report is readily available on request. The report sample contains a brief introduction to the research report, a Table of Contents, a Graphical introduction of regional analysis, Top players in the market with their revenue analysis, and our research methodology.)

Our Sample Report May Includes:

• 2030 Updated Report Introduction, Overview, and In-depth industry analysis.
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• 2023 Updated Regional Analysis with Graphical Representation of Size, Share & Trends
• Includes Updated List of tables & figures.
• Updated Report Includes Top Market Players with their Business Strategy, Sales Volume, and Revenue Analysis.

Artificial Intelligence in Digital Genome Market Market Competitive Analysis:

Know your current market situation! Not just new products but ongoing products are also essential to analyze due to ever-changing market dynamics. The study allows marketers to understand consumer trends and segment analysis where they can face a rapid market share drop. Figure out who really the competition is in the marketplace, get to know market share analysis, market position, % Market Share, and segmented revenue.

Players Included in Research Coverage: 
★ NVIDIA Corporation
★ IBM
★ Microsoft
★ Fabric Genomics Inc.
★ Verge Genomics
★ MolecularMatch Inc.
★ SOPHiA GENETICS
★ PrecisionLife Ltd
★ BenevolentAI
★ Deep Genomics

Additionally, Past Artificial Intelligence in Digital Genome Market Market data breakdown, Market Entropy to understand development activity and Patent Analysis*, Competitors Swot Analysis, Product Specifications, and Peer Group Analysis including financial metrics are covered.

Segmentation and Targeting:

Essential demographic, geographic, psychographic, and behavioral information about business segments in the Artificial Intelligence in Digital Genome Market market is targeted to aid in determining the features the company should encompass in order to fit into the business's requirements. For the Consumer-based market - the study is also classified with Market Maker information in order to understand better who the clients are, their buying behavior, and patterns.
 
By Offering:

• Software
• Services

By Technology:

• Machine Learning
• Computer Vision

By Functionality:

• Genome Sequencing
• Gene Editing

By Application:

• Diagnostics
• Drug Discovery & Development
• Precision Medicine
• Agriculture & Animal Research
• Other Applications

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Here are some ways AI is applied in digital genomics:

Genomic Data Analysis: AI algorithms and machine learning techniques are used to analyze large-scale genomic datasets, identifying patterns, variations, and associations within the data. This includes tasks such as genome alignment, variant calling, gene expression analysis, and identification of regulatory elements.

Variant Interpretation: AI can aid in the interpretation of genetic variants by integrating various data sources, including population genetics, functional genomics, and clinical databases. It can help prioritize and classify variants based on their potential impact on genes, proteins, and diseases.

Disease Diagnosis and Risk Prediction: AI models can be trained to predict disease risk based on genomic data. By learning patterns from large cohorts of genomic and clinical data, AI algorithms can identify genetic markers associated with specific diseases and provide risk assessment for individuals.

Precision Medicine: AI enables the development of personalized treatment plans based on an individual's genomic information. It can analyze genomic data to identify potential therapeutic targets, predict drug response, and optimize treatment options based on the patient's genetic profile.

Drug Discovery and Development: AI is used in computational drug discovery to identify novel drug targets, design drug candidates, and optimize drug properties. Genomic data, along with other omics data, can be integrated to predict drug efficacy and safety, accelerating the drug development process.

Genomic Data Privacy and Security: AI techniques can be employed to protect patient privacy and secure genomic data. Privacy-preserving algorithms and secure computation methods ensure that sensitive genomic information is anonymized and encrypted to prevent unauthorized access.

Clinical Decision Support: AI systems can provide clinicians with evidence-based recommendations by analyzing genomic data and correlating it with clinical knowledge. This assists in clinical decision-making, treatment planning, and patient management.

Genomic Data Visualization: AI can help visualize complex genomic data in intuitive and informative ways, aiding researchers and clinicians in understanding patterns and relationships within the data. This includes visualizations of genetic variation, gene expression profiles, and regulatory networks.

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