AI in Genomics Market To Cross USD 35,267.3 Million by 2033

Trishita Deb
Trishita Deb

Updated · Apr 2, 2024

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Introduction

The AI in Genomics market is projected to increase from USD 733.4 million in 2023 to approximately USD 35,267.3 million by 2033, with a CAGR of 47.3%. This significant growth trajectory highlights the pivotal role of AI in genomics, driven by the exponential increase in biomedical research and genomic data generation.

Several growth factors are propelling this market forward. The integration of AI with advanced genomic sequencing technologies is a critical driver, enhancing the efficiency and accuracy of identifying genetic disorders, predicting disease progression, and refining gene editing tools. The surge in AI adoption rates, which have increased by 4% globally in 2022, is a testament to AI’s growing significance in various sectors, including genomics. Additionally, the demand for personalized medicine, employing AI for more precise and individualized treatment approaches, is significantly contributing to the market’s expansion.

Veracyte, a leader in cancer diagnostics, has recently acquired C2i Genomics, a company specializing in tests for detecting minimal residual disease (MRD) in cancer patients. The deal, valued at $95 million, signifies Veracyte’s commitment to expanding its diagnostics portfolio and enhancing its capabilities in MRD detection and monitoring. Veracyte agreed to an upfront payment of $70 million in shares, with an additional $25 million set aside for performance milestones over two years post-acquisition, to be paid in either shares or cash.

Veracyte’s acquisition of C2i Genomics is poised to strengthen its product offerings by integrating C2i Genomics’ advanced whole-genome MRD tests, which leverage artificial intelligence to analyze small volumes of blood for cancer cell remnants post-treatment. The move not only enhances Veracyte’s position in the cancer care continuum but also underscores the growing importance of AI-driven technologies in improving patient outcomes through earlier MRD detection, offering faster results with less invasive sample requirements.

Key Takeaways

  • The AI in Genomics market is projected to witness substantial growth, with its value estimated to increase from USD 733.4 Million in 2023 to approximately USD 35,267.3 Million by the year 2033.
  • This growth is expected to occur at an impressive CAGR of 47.3% during the forecast period spanning from 2024 to 2033.
  • In terms of market segmentation based on components, the software segment emerged as the dominant segment in 2023, accounting for 47.2% of the market share.
  • Among the technology segments, machine learning stood out as the leading technology in 2023, capturing the largest market share.
  • Functionality-wise, genome sequencing held the largest market share of 46.1% in 2023.
  • The drug discovery and development application segment contributed the most to the market, with a market share exceeding 34.4%.
  • End-users in the AI in Genomics market primarily comprised pharmaceutical and biotech companies, accounting for a substantial market share in 2023.
  • Geographically, North America led the market by generating the highest revenue share, capturing 31.7% of the market in 2023.
  • The National Human Genome Research Institute estimates that the data anticipated to be generated from genomics research over the next decade will range from 2 to 40 billion exabytes.
  • Research published in the Proceedings of the National Academy of Sciences (PNAS) suggests that approximately 17.3% of adults, equivalent to 1 in 6 individuals, have genetic findings, with this proportion increasing to 11.5% (1 in 9 people) when combined with deep phenotyping data.
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AI in Genomics Statistics

  • Decrease in Genome Sequencing Cost: The price to sequence a genome has drastically reduced from about $300 million for the first one to now just $100.
  • Diversity in Genomic Data in the U.S.: The All of Us project in the United States now has over 250,000 genomes, focusing on including a variety of racial and ethnic groups.
  • UK Biobank’s Contribution: The UK Biobank is a significant initiative that shares a large amount of genomic data, providing valuable resources for research.
  • Genomic Data vs. Other AI Domains: Despite millions of genomes available, it’s still less compared to billions of data points used in other AI domains like image recognition and natural language processing.
  • AI in Genomics: Global firms are increasingly using AI to manage the vast data from genome sequencing, which can be around 100 terabytes per individual genome.

Recent Developments

  • Intel and University of Pennsylvania Collaboration: Intel Labs and the Perelman School of Medicine at the University of Pennsylvania have completed a joint research study utilizing distributed machine learning AI approaches for diagnosing brain tumors across international hospitals and research laboratories as of December 2022..
  • NVIDIA’s Partnership with MIT and Harvard’s Broad Institute: In September 2022, NVIDIA collaborated with MIT and Harvard’s Broad Institute to expedite genome analysis. This partnership focuses on integrating Clara Parabricks into Terra, developing advanced language models for targeted therapies, and enhancing deep learning for genomic analysis toolkit.
  • Technological Advancements in Genome Sequencing: An explosion in bioinformatics data is challenging genome analysis pipelines, requiring accelerated computing, data science, and AI to efficiently analyze genome sequencing data. NVIDIA is at the forefront, enhancing genome sequencing analysis workflows through deep learning and other advanced computational methods, significantly reducing the time and increasing the accuracy of genomic data analysis.
  • Funding and Investments in AI Genomics: TechCrunch reports a substantial investment by Amazon, putting another $2.75 billion into Anthropic AI as part of the current AI innovation wave. This move underscores the significant financial backing AI and genomics projects are receiving, indicating strong market confidence in the transformative potential of these technologies..
  • Skyflow’s Series B Funding: Skyflow raised an additional $30 million in Series B funding, led by Khosla Ventures. This investment highlights the growing demand for privacy business solutions in the context of AI’s rapid development..
  • Google.org’s Generative AI Accelerator Program: Google.org launched a $20 million generative AI accelerator program to support nonprofits developing technology that leverages generative AI. This initiative aims to foster innovative applications of AI in the nonprofit sector.

Key Players Analysis

  • IBM has established a strong presence in the AI in Genomics sector by utilizing its expertise in artificial intelligence to drive innovation and growth. As per the 2023 Annual Report, IBM is committed to enhancing its AI capabilities, particularly through its Watsonx platform, which is specifically designed to manage the entire lifecycle of AI for businesses, including genomics applications. This commitment is further substantiated by IBM’s significant investments in research and development, amounting to nearly $7 billion in 2023, highlighting its dedication to advancing AI technologies.
  • Microsoft Corporation leads the way in integrating artificial intelligence (AI) into the genomics field. They offer innovative services that empower researchers and clinicians to achieve faster, more efficient genomic data analysis and insights. The Microsoft Genomics service, which is part of their Healthcare NExT initiative, provides a cloud-based platform that supports genome sequencing and research. This service utilizes the Burrows-Wheeler Aligner (BWA) and the Genome Analysis Toolkit (GATK) to analyze genomic data, ensuring scalability, security, and compliance with health data standards such as ISO certification and HIPAA. Microsoft’s collaboration with institutions like St. Jude Children’s Research Hospital highlights their commitment to facilitating global research collaborations to accelerate discoveries in pediatric cancer and other genetic diseases.
    • The Genomics service is built on Microsoft’s Azure platform, which offers a robust infrastructure for the large-scale computational requirements of genomic data analysis. This enables researchers to manage, analyze, and share genomic information more effectively, leveraging Microsoft’s extensive network of data centers to meet data sovereignty and compliance requirements. The service is designed to be user-friendly, with an easy-to-use API for seamless integration into existing genomic analysis pipelines. This ensures that researchers can focus on their scientific inquiries rather than the complexities of data management.
  • NVIDIA Corporation is making significant progress in the AI in Genomics industry by using its advanced computing technologies and deep learning capabilities to enhance genomics research and analysis. NVIDIA has introduced powerful tools like NVIDIA Parabricks, which allows genomic researchers to perform analysis workflows up to 80 times faster, helping to quickly identify genetic variants that may lead to diseases or can be used as targets for therapeutics. The company’s integration of AI into genomics is transforming the field not only by accelerating whole genome sequencing analysis but also by improving the accuracy of these analyses, making both short and long-read sequencing platforms operate more efficiently. NVIDIA’s collaboration with biotech companies like PacBio has resulted in the development of new sequencing systems that can produce high-accuracy long reads, making it possible to sequence the human genome at scale for less than $1,000. This innovative approach helps deal with the massive data generated by genomics, addresses storage challenges, and optimizes computational processes for faster and more precise results. With these initiatives, NVIDIA is leading the way in the next generation of genomics, significantly impacting clinical workflows, drug discovery, and the broader healthcare and life sciences industries.
  • Deep Genomics is revolutionizing drug development by leveraging artificial intelligence to navigate the complexities of RNA biology. Their AI platform excels at identifying novel therapeutic targets and evaluating numerous potential therapies to pinpoint the most promising candidates. In 2018, this platform began identifying targets related to RNA splicing defects, leading to the introduction of the first Foundation Model for RNA Biology, BigRNA, in 2023. Deep Genomics is now developing BigRNA+, aiming to tackle more complex genetic diseases by discovering new biology and genetic targets.
  • Data4Cure, Inc. utilizes the Biomedical Intelligence® Cloud to transform data into actionable insights for drug development. This platform supports pharmaceutical companies and research organizations in making critical decisions throughout the drug discovery process, employing bioinformatics, systems biology, machine learning, and natural language processing. Its CURIE Knowledge Graph™ integrates vast datasets and publications, facilitating a comprehensive understanding of disease mechanisms, target identification, and drug repositioning.
  • Freenome Holdings, Inc. has raised $254 million to advance its early cancer detection platform, focusing on colorectal and lung cancers using blood tests. This funding will enhance the development of single-cancer and tailored multi-cancer early detection tests built on its multiomics platform. With support from leading investors including Roche, Freenome aims to make early cancer detection more convenient and accessible, leveraging its platform to develop non-invasive screening tools for early, treatable stages of cancer.
  • Thermo Fisher Scientific is enhancing genetic research with artificial intelligence (AI) and automation, particularly in its microarray platform. These innovations aim to boost lab productivity by efficiently analyzing and interpreting vast amounts of genetic data. AI plays a crucial role in genomics for identifying genetic disorders and improving disease research. Thermo Fisher’s approach combines molecular assays with computational biology and machine learning to handle the complexity of genetic data, promising advancements in early genome reading and disease research.
  • Illumina, Inc. has developed PrimateAI-3D, an AI algorithm for predicting disease-causing genetic mutations with high accuracy, demonstrated in research published in Science. This advancement, leveraging deep neural network architectures, aims to enhance genetic risk prediction and drug target discovery, facilitating precision medicine and understanding complex genetic diseases.
  • SOPHiA GENETICS is revolutionizing AI-assisted medicine by integrating and analyzing healthcare-omics data from various modalities. Their mission focuses on breaking down data silos and utilizing machine learning to deliver actionable insights, ultimately aiming to enhance patient outcomes globally.
  • BenevolentAI utilizes AI to empower scientists in unveiling new insights from data, significantly accelerating innovation and the discovery of successful drugs. Their AI-enabled drug discovery engine, the Benevolent Platform™, leverages vast biomedical data to offer a multidimensional view of human biology across diseases, aiming to enhance the success rate of clinical developments.
  • Fabric Genomics specializes in AI-driven genomic interpretation, helping clinical labs, hospital systems, and national sequencing programs harness next-generation sequencing (NGS) data for precise diagnostics and treatments. Their technology is recognized for best-in-class accuracy and scalability in analyzing whole genomes, exomes, and targeted panels for various conditions, including rare diseases, hereditary risks, and cancers.

Conclusion

The Genomics market is on the brink of significant growth in Artificial Intelligence (AI), driven by technological advancements, a growing need for personalized medicine, and considerable investments in research and development. However, this growth will present its set of challenges, requiring innovative solutions and collaborations across the scientific community to utilize the potential of AI in genomics fully.

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Trishita Deb

Trishita Deb

Trishita has more than 7 years of experience in market research and consulting industry. She has worked in various domains including healthcare, consumer goods, and materials. Her expertise lies majorly in healthcare and has worked on more than 400 healthcare reports throughout her career.