基因组学人工智慧市场：按应用、人工智慧技术、服务、序列类型和最终用户划分-全球预测，2025-2032年

预计到 2032 年，基因组学领域的人工智慧市场将成长至 75.3014 亿美元，复合年增长率为 33.63%。

关键市场统计数据
基准年 2024	7.4023亿美元
预计年份：2025年	9.8496亿美元
预测年份 2032	75.3014亿美元
复合年增长率 (%)	33.63%

先进计算建模、高通量测序和数据工程的融合如何重新定义基因组学领域的发现、诊断和治疗决策

人工智慧正透过将演算法的严谨性与生物学洞察力结合，迅速重塑基因组学，使以往难以实现的发现成为可能。模型架构的进步、註释资料集的日益丰富以及云端原生运算生态系统的构建，共同提升了基因组讯号解读的速度和准确性。计算方法与高通量定序的融合，为理解遗传变异、识别治疗标靶以及将分子特征转化为临床可操作的决策提供了新的途径。

此外，在诊断领域，临床和研究专用检测中模式识别技术的进步正在缩短结果解读等待时间。在药物研发领域，计算模型正在简化先导化合物的筛选流程，改善标靶检验，并提高临床前试验的效率。在精准医疗领域，预测演算法正在为伴随诊断的开发提供讯息，塑造个人化治疗策略，并支持药物基因组学决策。

本导言执行摘要的其余部分奠定了基础，重点阐述了演算法创新、资料保真度和服务交付之间的相互作用。它强调，持续进步将取决于稳健的註释和解读实践、跨定序平台的整合以及来自学术界、临床机构和产业界的相关人员之间的合作。因此，领导者在将人工智慧融入基因组学工作流程时，必须同时考虑技术机会和操作复杂性。

新的演算法架构、整合的序列到分析工作流程以及协作模式加速了翻译进程，同时合规性和验证也成为更重要的优先事项。

基因组学领域正经历着变革性的转变，这主要得益于模型能力的提升、多模态资料集的丰富以及端到端运算流程的日趋成熟。深度学习架构，包括卷积神经网络和循环神经网络，如今已广泛应用于需要空间模式识别和时间序列解释的任务；而自编码器则有助于降维和潜在表征学习，从而揭示隐藏的生物学关係。机器学习范式，例如监督学习和无监督学习，仍然是分类和丛集任务的基础；强化学习也开始在高通量环境下指导实验设计和资源分配。应用于生物医学文献和临床记录的自然语言处理技术正在改善资讯搜寻并加速假设生成。

方法论的这种转变与服务创新同步发生。生物资讯服务正朝着模组化和云端整合的方向发展，使得註释流程和解读引擎能够以可扩展服务的形式提供，而非客製化计划。定序服务正日益与分析平台结合，从而使EXOME、转录组和全基因组的输出能够直接流入经过检验的计算工作流程。咨询服务也正从单纯的效能交付转向涵盖资料管治、模型检验和部署流程的策略伙伴关係关係。

在营运层面，学术机构、临床实验室和商业实体正朝着更整合的协作模式发展，透过存取控制机制共用。这种转变减少了重复工作，加快了模型训练速度，并提高了可重复性。同时，对可解释性、溯源性和合规性日益增长的需求，推动了标准化本体、版本化流程和严格检验框架的采用。这些变革性趋势不仅能够更快地将实用化，也提高了品质保证和伦理管理的标准。

美国累积关税措施对基因组研究供应链、筹资策略、合作研究实践和国内创新重点的定性影响

2025年美国关税政策的动态为基因组学领域的供应链、采购决策和研究合作带来了一系列复杂的定性压力。这些压力的累积效应加剧了跨境采购的敏感性，促使研究机构重新审视供应商选择标准，并评估试剂、仪器和计算设备供应管道的韧性。事实上，这已促使许多相关人员加快供应商多元化的步伐，并探索关键耗材和设备的本土化生产和区域製造伙伴关係。

同时，规模较小的实验室和研究团队正在探索联合采购联盟和替代筹资策略，以降低成本波动。这些措施正在重塑供应商关係，并将商业性对话的重点转向整体拥有成本、前置作业时间保证和服务水准承诺。

研究合作和资料共用安排也在不断调整。以往跨境计划依赖快速的试剂补给和仪器服务合同，而现在团队更加重视数据可移植性和远端分析能力，将其作为应急机制。当实体元件因海关原因延误时，云端原生分析平台和软体即服务（SaaS）已成为维持专案连续性的关键。同时，人们对智慧财产权和资料在地化的日益关注，也促使人们重新重视更严格的合约框架和本地监管合规性。

在创新方面，关税压力正促使国内投资于替代技术，包括定序耗材的生产、更易于在地采购的模组化仪器设计，以及减少对专有硬体依赖的软体平台。虽然这些转变并不能消除专业化和规模经济带来的权衡取舍，但它们正在重塑竞争格局，并鼓励专注于提供高性价比本土解决方案的新进业者。最终，2025年关税的累积影响加速了供应链的策略性重新评估，提升了整合分析服务的价值，并提高了基因组学工作流程中营运韧性的重要性。

透过细分市场主导了解应用需求、演算法方法、服务模式、定序方式和最终用户优先顺序将如何决定基因组人工智慧的采用路径。

透过精细的细分视角，我们可以了解基因组人工智慧在临床、农业和商业领域中最具实际价值的应用方向。应用领域包括农业和动物基因组学，它们受益于演算法性状选择和基因组选择方法，加速作物改良和牲畜育种，并使育种者能够更有效地优先考虑产量、适应性和抗病性。诊断领域涵盖临床诊断实验室和研究诊断团队。人工智慧透过改进变异解读和缩短週转时间，对高通量检测进行了补充。研究诊断利用模式发现来产生用于后续检验的假设。药物发现将计算方法扩展到先导化合物识别、标靶验证和临床前检验，人工智慧模型增强了虚拟筛检、预测脱靶效应并优化了实验优先顺序。精准医疗整合了伴随诊断、个人化治疗和药物基因组学，以基于先导化合物和临床数据相结合识别的预测性生物标记来客製化治疗方案。

在人工智慧技术领域，深度学习的进步，包括自编码器、卷积类神经网路和循环神经网络，对基于序列的模式识别和表征学习产生了特别显着的影响。机器学习的子领域，例如监督学习、无监督学习和强化学习，是分类、丛集和最佳化实验策略的核心。应用于文献挖掘和临床文本的自然语言处理技术，能够从非结构化资料来源中提取可操作的见解，从而促进快速的证据整理并支持转化研究。

以服务为导向的细分强调了整合服务的重要性。生物资讯服务提供註释、资料分析和解读，是把原始序列转化为可解释结果的基础。咨询服务提供实施支援和策略制定，帮助企业将技术部署与临床和商业性目标保持一致。定序服务涵盖EXOME定序、转录组定序和全基因定序，为下游分析奠定基础。软体和平台的选择（无论是云端基础还是本地部署）决定了可扩展性、资料管治和延迟情况。

定序方式的差异对分析流程和采购都至关重要。新一代定序平台，例如 Illumina、Ion Torrent 和 PacBio，提供多种读长、通量和错误率，从而影响模型训练和结果解读策略。使用毛细管和萤光定序的 Sanger 定序仍然是验证和标靶分析的重要方法。学术和研究机构，包括研究所和大学，优先考虑方法的开放性和可重复性。医院和诊所，包括诊断实验室和医疗中心，优先考虑法规遵循、週转时间和与临床工作流程的整合。製药和生物技术公司，包括生物技术公司和大型製药公司，需要可扩展的流程、智慧财产权保护和符合监管标准的验证，以支持药物开发和伴随诊断策略。

综上所述，这些细分洞察表明，人工智慧在基因组学领域的成功部署需要对技术选择、服务模式、定序方式和最终用户优先顺序的细緻协调。针对特定应用需求和营运限制量身定制的解决方案将获得更高的采用率和更大的后续影响。

区域市场动态如何影响美洲、欧洲、中东和非洲以及亚太地区的基因组人工智慧商业化、监管和合作

地理动态对全球基因组学生态系统的投资模式、法规环境和合作研究有显着影响。美洲地区持续展现出产业界、学术中心和临床系统之间的紧密整合，拥有成熟的创投倡议支援转化研究，以及强大的云端基础分析基础设施。儘管这种环境有利于人工智慧工具的快速商业化，但也面临日益严格的监管审查，以及对资料安全和病患知情同意框架日益增长的重视。

欧洲、中东和非洲地区（EMEA）的监管体系复杂多样，各国在报销和临床应用方面既存在差异，也存在协调统一的努力。公共部门对基因组学和合作研究联盟的投资是该地区的显着特征，该地区高度重视资料保护、伦理管治和互通性标准。这些优先事项正在影响供应商的策略，促使他们开发优先考虑隐私保护分析、透明溯源和符合当地卫生部门要求的解决方案。

亚太地区拥有高通量测序能力、部分市场强大的本土製造业，以及正在加速的官民合作关係项目，这些都推动了大规模基因组学计画的实施。各国政府致力于利用基因组学实现国家健康和粮食安全目标，这促进了临床基因组学和农业领域的快速发展。此外，该地区还拥有日益壮大的人工智慧人才和云端基础设施提供商生态系统，这些因素共同推动了本地化创新、加快了迭代周期，并为现有供应商提供了更具竞争力的替代方案。

在这些全部区域，跨境合作依然存在，但日益受到数据主权、供应链韧性和监管协调等因素的限制。能够兼顾当地采购惯例、临床检验要求以及数据使用方面的文化预期等因素的区域策略，在市场渗透和持续影响方面都将具有显着优势。

平台巨头、专业设备製造商、云端服务供应商和创新新兴企业如何透过伙伴关係、检验和营运弹性来塑造竞争格​​局

人工智慧基因组学领域的竞争格局将由平台巨头、专业仪器製造商、云端处理供应商以及新兴新新兴企业共同构成，这些企业将各自在领域专业知识和创新演算法方法方面相结合。平台巨头将提供包含定序、分析和支援服务的整合解决方案，而专业仪器製造商则专注于提升通量、准确性和耗材成本。云端运算和运算提供者将支援可扩展的模型训练和推理，从而降低缺乏大规模本地基础设施的机构的准入门槛。

新兴企业和专业供应商正透过新的模型架构、目标资料集和服务产品实现差异化，以解决特定的痛点，例如临床级可解释性、低资源部署以及用于分散式检查的边缘分析。仪器製造商和软体供应商之间的伙伴关係日益普遍，这反映出业界对端到端检验解决方案的偏好，这些解决方案可以降低最终用户的整合风险。学术衍生公司和联盟主导的倡议继续支持创新进程，它们通常与商业实体合作，以推进研究成果的检验和监管批准。

成功的公司将具备卓越的技术实力、可重复的检验机制、完善的资料管治实务以及清晰的终端用户价值提案。投资于透明的模型文件、基于独立数据集的严格基准测试以及与临床或农业合作伙伴开展的合作试验的公司，将更有利于克服推广应用方面的障碍。同样重要的是，要建立策略联盟，以确保供应链的连续性和区域影响力。

为高阶主管提供切实可行的、循序渐进的策略建议，以确保人工智慧在基因组学领域透过资料管治、供应链弹性、模组化架构和道德管理发挥价值。

产业领导者应采取务实、分阶段的方法将人工智慧融入基因组学，在创新与营运严谨性之间取得平衡。首先，要明确与组织能力和监管限制相符的高影响力应用案例，然后优先投资于能够在这些应用案例中带来可复製价值的项目。早期工作应着重于建立健全的资料管理、溯源追踪和标註标准，以确保模型基于可靠且文件齐全的资料集进行训练。

研发领导者还应制定混合采购策略，透过结合区域供应商、关键耗材的长期合约以及云端基础的运算和分析容错移转方案来降低供应链风险。与学术中心和临床实验室建立策略伙伴关係可以加快检验，并提供获取多样化资料集的途径。

从技术角度来看，应采用模组化架构，使团队能够在不中断检验工作流程的情况下更换模型元件和序列输入。应重视可解释性和文件记录，以促进监管审查和临床医生的认可，并投资于持续监测和部署后检验，以检测模型漂移并维持其性能。最后，应将伦理管治和隐私保护技术融入专案设计，以建立与病患、监管机构和商业伙伴的信任。采取这些措施将有助于组织充分利用人工智慧的优势，同时管控基因组学应用中固有的营运和声誉风险。

混合方法研究框架，结合专家访谈、文献综述、独立基准化分析、情境分析和伦理管治审查，以提供可靠且可操作的见解。

本分析的调查方法结合了定性专家访谈、技术文献的系统性回顾以及相关人员访谈检验，以确保结论的全面性和平衡性。主要见解来自于与来自学术界、临床诊断、设备製造和软体开发等不同领域的专家的结构化对话。此外，还对同行评审的研究、技术预印本、监管指导文件和公开的产品规格进行了严格的审查，以确保技术评估是基于最新证据。

为确保分析的严谨性，我们采用交叉检验，将演算法声明与独立的基准资料集进行比对，并对报告的模型架构和效能指标进行可复现性检查。我们结合采购案例、供应商文件和实际实施报告，对服务和商业化的洞察进行三角验证，以捕捉现实世界的限制因素。此外，分析还包含基于情境的思考，以探索应对外部压力（例如供应链中断和不断变化的监管预期）的营运回应措施。

调查方法明确纳入了伦理和隐私方面的考量，包括评估资料管治框架、同意机制以及联邦学习和安全飞地等隐私保护计算技术。研究的局限性和不确定领域也已记录在案，以便读者评估研究结果的实际适用性，并确定后续基础研究和试点工作的优先事项。

将技术前景与营运需求相结合，以展示数据品质、互通性和协作验证如何推动基因组检验在现实世界中产生影响。

人工智慧正在加速基因组学的变革，显着提升了生物学假设的生成、检验和转化速度及准确性。这项技术正推动更精准的农业育种、更快更准确的诊断、更简化的药物研发流程以及日益个人化的治疗策略。然而，进步并非仅仅取决于模型的复杂程度；它同样依赖资料品质、定序平台与分析工具之间的互通性，以及能够应对监管和供应链波动的灵活运作模式。

展望未来，那些将技术严谨性与务实营运策略（例如强大的资料管理、模组化技术架构、区域资料来源多元化和透明的检验实践）相结合的机构，将最有能力实现持续的影响力。学术界、临床系统、产业界和政策制定者之间的合作仍然至关重要，这有助于协调奖励、加快检验週期，并确保伦理和隐私方面的考量不会阻碍技术进步。专注于人工智慧整合的科学和营运层面，将使相关人员能够把计算前景转化为稳健的、实际的基因组解决方案。

目录

第一章：序言

第二章调查方法

第三章执行摘要

第四章 市场概览

第五章 市场洞察

• 深度学习演算法提高了临床诊断中对罕见基因组变异的解读能力。
• 利用大规模基因组资料集辨识标靶的AI驱动药物发现平台
• 将多组体学分析与机器学习模型结合，实现个人化治疗优化
• 一种联邦学习框架，可在不共用资料的情况下实现安全的跨机构基因组资料分析。
• 可解释人工智慧解决方案，提供透明的基因组变异分类，以满足监管合规要求
• 人工智慧驱动的单细胞定序揭示复杂疾病中的细胞异质性
• 云端基础、人工智慧驱动的基因组学流程，为高通量测序提供可扩展的基础设施
• 利用多基因风险评分的预测基因组学模型预测族群疾病感受性
• 应用深度强化学习优化 CRISPR Cas9 在基因编辑中的脱靶预测
• 利用人工智慧挖掘基因组和临床健康记录数据，以产生真实世界证据

第六章：美国关税的累积影响，2025年

第七章：人工智慧的累积影响，2025年

8. 基因组学人工智慧市场（按应用领域划分）

• 农业与动物基因体学
  • 作物改良
  • 畜牧业
• 诊断
  • 临床诊断
  • 调查和诊断
• 药物发现
  • 铅识别
  • 临床前试验
  • 目标检验
• 精准医疗
  • 伴随诊断
  • 个人化治疗
  • 药物基因组学

9. 人工智慧技术在基因体学市场的应用

• 深度学习
  • 自编码器
  • 卷积类神经网路
  • 循环神经网络
• 机器学习
  • 强化学习
  • 监督式学习
  • 无监督学习
• 自然语言处理
  • 情绪分析
  • 文字探勘

10. 基因组学人工智慧市场（按服务划分）

• 生物资讯服务
  • 评论
  • 数据分析
  • 解释
• 咨询
  • 实施支持
  • 策略发展
• 定序服务
  • EXOME定序
  • 转录组定序
  • 全基因测序
• 软体和平台
  • 云端基础的
  • 本地部署

11. 基因组学人工智慧市场（按序列类型划分）

• 次世代定序
  • Illumina
  • 离子洪流
  • 包装生物
• 桑格定序
  • 微血管
  • 萤光

第十二章 基因组学人工智慧市场（按最终用户划分）

• 学术研究
  • 研究所
  • 大学
• 医院和诊所
  • 诊断实验室
  • 医疗中心
• 製药和生物技术
  • 生技公司
  • 大型製药公司

13. 各地区基因体学人工智慧市场

• 美洲
  • 北美洲
  • 拉丁美洲
• 欧洲、中东和非洲
  • 欧洲
  • 中东
  • 非洲
• 亚太地区

第十四章 基因组学人工智慧市场（按群体划分）

• ASEAN
• GCC
• EU
• BRICS
• G7
• NATO

15. 各国基因组学人工智慧市场

• 美国
• 加拿大
• 墨西哥
• 巴西
• 英国
• 德国
• 法国
• 俄罗斯
• 义大利
• 西班牙
• 中国
• 印度
• 日本
• 澳洲
• 韩国

第十六章 竞争格局

• 2024年市占率分析
• FPNV定位矩阵，2024
• 竞争分析
  • F. Hoffmann-La Roche Ltd
  • Thermo Fisher Scientific Inc.
  • Illumina, Inc.
  • Agilent Technologies, Inc.
  • QIAGEN NV
  • Bio-Rad Laboratories, Inc.
  • PerkinElmer, Inc.
  • BGI Genomics Co., Ltd
  • NanoString Technologies, Inc.
  • SOPHiA Genetics SA

The Artificial Intelligence in Genomics Market is projected to grow by USD 7,530.14 million at a CAGR of 33.63% by 2032.

KEY MARKET STATISTICS
Base Year [2024]	USD 740.23 million
Estimated Year [2025]	USD 984.96 million
Forecast Year [2032]	USD 7,530.14 million
CAGR (%)	33.63%

How the convergence of advanced computational models, high-throughput sequencing, and data engineering is redefining discovery, diagnostics, and therapeutic decisions in genomics

Artificial intelligence is rapidly reshaping genomics by combining algorithmic rigor with biological insight to enable discoveries that were previously impractical. Advances in model architectures, increased availability of annotated datasets, and cloud-native compute ecosystems have collectively increased the speed and fidelity with which genomic signals can be interpreted. The convergence of computational methods and high-throughput sequencing has created new modalities for understanding genetic variation, identifying therapeutic targets, and translating molecular signatures into clinically actionable decisions.

Across applications, AI-driven approaches are enhancing capabilities in crop improvement and livestock breeding by enabling more precise trait selection and accelerated breeding cycles, while diagnostics benefit from improved pattern recognition across clinical and research-focused assays to reduce interpretation latency. In drug discovery, computational models are streamlining lead identification, refining target validation, and improving the efficiency of preclinical testing. Within precision medicine, predictive algorithms are informing companion diagnostic development, shaping personalized therapeutic strategies, and supporting pharmacogenomic decision-making.

This introduction frames the remainder of the executive summary by emphasizing the interplay between algorithmic innovation, data fidelity, and service delivery. It underscores that sustained progress will depend on robust annotation and interpretation practices, integration across sequencing platforms, and the alignment of stakeholders in academia, clinical settings, and industry. As a result, leaders must consider both technological opportunity and operational complexity when integrating AI into genomics workflows.

Emerging algorithmic architectures, integrated sequencing-to-analysis workflows, and federated collaboration models that are accelerating translation while raising compliance and validation priorities

The genomic landscape is undergoing transformative shifts driven by deeper model capacity, richer multimodal datasets, and the maturation of end-to-end computational pipelines. Deep learning architectures, including convolutional and recurrent networks, are now routinely applied to tasks that require spatial pattern recognition and temporal sequence interpretation, while autoencoders facilitate dimensionality reduction and latent representation learning that uncover hidden biological relationships. Machine learning paradigms such as supervised and unsupervised learning continue to underpin classification and clustering tasks, and reinforcement learning is beginning to inform experimental design and resource allocation in high-throughput settings. Natural language processing techniques applied to biomedical literature and clinical notes are improving information retrieval and accelerating hypothesis generation.

These methodological shifts are paralleled by service innovation. Bioinformatics services are becoming more modular and cloud-integrated, enabling annotation pipelines and interpretation engines to be consumed as scalable services rather than bespoke projects. Sequencing services are increasingly coupled to analytic platforms so that exome, transcriptome, and whole genome outputs flow directly into validated computational workflows. Consulting practices are transitioning from implementation-only engagements to strategic partnerships that encompass data governance, model validation, and deployment pipelines.

Operationally, the industry is moving toward a more federated model of collaboration where academic institutions, clinical laboratories, and commercial entities share curated datasets through controlled-access mechanisms. This shift reduces duplication of effort, accelerates model training, and enhances reproducibility. At the same time, demand for explainability, provenance tracking, and regulatory compliance is rising, prompting the adoption of standardized ontologies, versioned pipelines, and rigorous validation frameworks. Collectively, these transformative shifts are enabling faster translation of genomic insights into practical applications while raising the bar for quality assurance and ethical stewardship.

Qualitative implications of cumulative U.S. tariff measures on supply chains, procurement strategies, collaborative research practices, and domestic innovation priorities within genomics

U.S. tariff policy dynamics in 2025 have introduced a complex set of qualitative pressures across supply chains, procurement decisions, and research collaborations in genomics. The cumulative effect has been to increase sensitivity to cross-border sourcing, encouraging institutions to revisit vendor selection criteria and to evaluate the resilience of reagent, instrument, and compute supply channels. In practice, this has led many stakeholders to accelerate efforts to diversify suppliers and to explore onshoring or regional manufacturing partnerships for critical consumables and instruments.

From a procurement perspective, higher import levies and administrative friction have incentivized larger organizations to negotiate longer-term contracts to secure price stability, while smaller laboratories and research groups have sought collaborative purchasing consortia or alternative sourcing strategies to mitigate cost volatility. These behaviors are reshaping supplier relationships and shifting commercial conversations toward total cost of ownership, lead time guarantees, and service-level commitments.

Research collaborations and data-sharing arrangements have also adapted. Where cross-border projects previously relied on rapid reagent resupply and instrument service agreements, teams are now placing greater emphasis on data portability and remote analysis capabilities as contingency mechanisms. Cloud-native analysis platforms and software-as-a-service offerings have become essential for maintaining continuity when physical components face tariff-driven delays. At the same time, concerns around intellectual property and data localization have grown, prompting more rigorous contractual frameworks and a renewed focus on local regulatory compliance.

On the innovation front, tariff-induced pressures have spurred domestic investment in alternative technologies, including production of sequencing consumables, modular instrumentation designs that are easier to source locally, and software platforms that reduce reliance on proprietary hardware. While these shifts do not eliminate the tradeoffs associated with specialization and economies of scale, they are reshaping competitive positioning and encouraging new entrants focused on cost-effective domestic solutions. Ultimately, the cumulative impact of tariffs in 2025 has been to accelerate strategic reassessment of supply chains, strengthen the value of integrated analytic services, and increase the importance of operational resilience in genomics workflows.

Segmentation-driven insight into how application needs, algorithmic approaches, service models, sequencing modalities, and end-user priorities determine adoption pathways in genomic AI

A granular segmentation lens reveals where AI in genomics is generating the most actionable value across distinct clinical, agricultural, and commercial contexts. In application domains, agriculture and animal genomics benefit from algorithmic trait selection and genomic selection methods that accelerate crop improvement and livestock breeding, enabling breeders to prioritize yield, resilience, and disease resistance more effectively. Diagnostics encompasses both clinical diagnostic labs and research diagnostics teams; AI complements high-throughput assays by improving variant interpretation and reducing turnaround, while research diagnostics leverage pattern discovery to generate hypotheses for downstream validation. In drug discovery, computational approaches span lead identification, target validation, and preclinical testing, with AI models enhancing virtual screening, predicting off-target effects, and optimizing experimental prioritization. Precision medicine integrates companion diagnostics, personalized therapeutics, and pharmacogenomics to tailor treatments based on predictive biomarkers identified through combined genomic and clinical data.

Regarding AI techniques, advances in deep learning-encompassing autoencoders, convolutional neural networks, and recurrent neural networks-are particularly impactful for sequence-based pattern recognition and representation learning. Machine learning subfields such as supervised, unsupervised, and reinforcement learning remain core to classification, clustering, and optimized experimental strategies. Natural language processing techniques, applied to literature mining and clinical text, facilitate rapid curation of evidence and support translational research by extracting actionable insights from unstructured sources.

Service-oriented segmentation underscores the importance of integrated offerings. Bioinformatics services that deliver annotation, data analysis, and interpretation are foundational for transforming raw sequences into interpretable results. Consulting engagements that address implementation support and strategy development help organizations align technical deployments with clinical and commercial objectives. Sequencing services-spanning exome sequencing, transcriptome sequencing, and whole genome sequencing-feed downstream analytics, while software and platform choices, whether cloud-based or on-premise, determine scalability, data governance, and latency profiles.

Sequencing modality distinctions matter for both analytic pipelines and procurement. Next generation sequencing platforms such as Illumina, Ion Torrent, and PacBio deliver varied read lengths, throughput, and error profiles that influence model training and interpretation strategies. Sanger sequencing, with capillary and fluorescence modalities, continues to serve as a validation and targeted analysis approach. End-user segmentation further differentiates adoption patterns: academic and research institutions, including research institutes and universities, prioritize methodological openness and reproducibility; hospitals and clinics, including diagnostic laboratories and medical centers, emphasize regulatory compliance, turnaround time, and integration with clinical workflows; and pharma and biotech organizations, both biotech firms and large pharmaceutical companies, require scalable pipelines, IP protection, and regulatory-grade validation to support drug development and companion diagnostic strategies.

Taken together, these segmentation insights illustrate that successful AI adoption in genomics requires a nuanced alignment of technique selection, service model, sequencing modality, and end-user priorities. Solutions tailored to the specific combination of application needs and operational constraints will achieve higher adoption and greater downstream impact.

Regional market dynamics shaping commercialization, regulation, and collaborative research in genomic AI across the Americas, EMEA, and Asia-Pacific landscapes

Geographic dynamics materially influence investment patterns, regulatory environments, and collaborative behaviors across the global genomics ecosystem. The Americas continue to demonstrate a strong integration between industry, academic centers, and clinical systems, with mature venture capital networks supporting translational initiatives and robust infrastructure for cloud-based analytics. This environment favors rapid commercialization of AI-driven tools, although it also faces heightened regulatory scrutiny and increasing emphasis on data security and patient consent frameworks.

Europe, Middle East & Africa presents a diverse regulatory mosaic where harmonization efforts coexist with country-level variability in reimbursement and clinical adoption pathways. Public sector investment in genomics and collaborative consortia is a notable feature, and the region places strong emphasis on data protection, ethical governance, and interoperability standards. These priorities shape vendor strategies, encouraging solutions that prioritize privacy-preserving analytics, transparent provenance, and compliance with local health authority requirements.

Asia-Pacific is characterized by a mix of high-throughput sequencing capacity, strong domestic manufacturing in certain markets, and accelerating public-private partnerships that drive large-scale genomic initiatives. Rapid adoption in clinical genomics and agriculture is supported by governments seeking to leverage genomics for national health and food security goals. The region also demonstrates a growing ecosystem of AI talent and cloud infrastructure providers, which together enable localized innovation, faster iteration cycles, and competitive alternatives to incumbent suppliers.

Across these regions, cross-border collaborations persist but are increasingly mediated by considerations of data sovereignty, supply chain resilience, and regulatory alignment. Regional strategies that account for local procurement practices, clinical validation requirements, and cultural expectations around data use will have a distinct advantage in both market penetration and sustained impact.

How platform incumbents, specialized instrument makers, cloud providers, and innovative startups are structuring the competitive landscape through partnerships, validation, and operational resilience

Competitive dynamics in AI-enabled genomics are defined by a mix of platform incumbents, specialized instrument makers, cloud and compute providers, and emerging startups that combine domain expertise with novel algorithmic approaches. Platform incumbents bring integrated solutions that bundle sequencing, analytics, and support services, while specialized instrument manufacturers focus on improvements in throughput, accuracy, and consumable economics. Cloud and compute providers enable scalable model training and inference, lowering barriers for organizations without extensive on-premise infrastructure.

Startups and specialist vendors are differentiating through novel model architectures, targeted datasets, and service offerings that address specific pain points such as clinical-grade interpretability, low-resource deployment, and edge-enabled analytics for decentralized testing. Partnerships between instrument manufacturers and software providers are increasingly common, reflecting the industry preference for end-to-end validated solutions that reduce integration risk for end users. Academic spinouts and consortium-driven initiatives continue to feed the innovation pipeline, often partnering with commercial entities to move discoveries through validation and regulatory pathways.

Successful companies are those that combine technical excellence with reproducible validation regimes, strong data governance practices, and clear value propositions for distinct end users. Firms that invest in transparent model documentation, rigorous benchmarking against independent datasets, and collaborative trials with clinical or agricultural partners are better positioned to overcome adoption barriers. Equally important are strategic alliances that secure supply chain continuity and regional presence, as these operational factors are increasingly influential in procurement decisions.

Practical and phased strategic recommendations for executives to secure value from AI in genomics through data governance, supply resilience, modular architectures, and ethical stewardship

Industry leaders should adopt a pragmatic, phased approach to integrating AI into genomics that balances innovation with operational rigor. Start by defining high-impact use cases that align with organizational capabilities and regulatory constraints, and then prioritize investments that deliver reproducible value within those use cases. Early efforts should focus on establishing robust data curation, provenance tracking, and annotation standards to ensure that models are trained on reliable, well-documented datasets.

Leaders should also develop a hybrid sourcing strategy that mitigates supply chain risk by combining regional suppliers, long-term contracts for critical consumables, and cloud-based failover options for compute and analytics. Strategic partnerships with academic centers and clinical laboratories can accelerate validation and provide access to diverse datasets, while consulting engagements can bridge capability gaps during implementation.

From a technology perspective, adopt modular architectures that allow teams to swap model components and sequencing inputs without disrupting validated workflows. Emphasize explainability and documentation to facilitate regulatory review and clinician acceptance, and invest in continuous monitoring and post-deployment validation to detect model drift and maintain performance. Finally, embed ethical governance and privacy-preserving techniques into program design to build trust with patients, regulators, and commercial partners. These steps will help organizations capture the benefits of AI while managing the operational and reputational risks inherent in genomics applications.

A mixed-methods research framework combining expert interviews, literature synthesis, independent benchmarking, scenario analysis, and ethical governance review to ensure robust actionable insights

The research methodology underpinning this analysis combined qualitative expert elicitation, systematic evaluation of technical literature, and validation through stakeholder interviews to ensure comprehensive and balanced conclusions. Primary insights were derived from structured conversations with domain experts spanning academia, clinical diagnostics, instrument manufacturing, and software development. These interviews were complemented by a rigorous review of peer-reviewed studies, technical preprints, regulatory guidance documents, and publicly disclosed product specifications to ground technical assessments in current evidence.

Analytic rigor was maintained through cross-validation of algorithmic claims against independent benchmarking datasets and by applying reproducibility checks to reported model architectures and performance metrics. Service and commercialization insights were triangulated using procurement case studies, vendor documentation, and practical implementation reports to capture real-world constraints. The analysis also included scenario-based thinking to explore operational responses to external pressures such as supply chain disruptions and evolving regulatory expectations.

Ethical and privacy considerations were explicitly integrated into the methodology. This involved evaluating data governance frameworks, consent mechanisms, and privacy-preserving computational techniques such as federated learning or secure enclaves. Limitations and areas of uncertainty were documented to help readers assess the contextual applicability of the findings and to identify priorities for additional primary research or pilot engagements.

Synthesis of technological promise and operational imperatives showing how data quality, interoperability, and collaborative validation determine real-world impact in genomic AI

AI is catalyzing a step-change in genomic science by improving the speed and fidelity with which biological hypotheses are generated, validated, and translated. The technology is enabling more precise agricultural breeding, faster and more accurate diagnostics, streamlined drug discovery workflows, and increasingly personalized therapeutic strategies. Progress, however, is not merely a function of model sophistication; it depends equally on data quality, interoperability between sequencing platforms and analytic tools, and resilient operational practices that accommodate regulatory and supply chain variability.

Looking ahead, organizations that combine technical rigor with pragmatic operational strategies-strong data stewardship, modular technical architectures, regional supply diversification, and transparent validation practices-will be best positioned to realize sustained impact. Collaboration across academia, clinical systems, industry, and policy makers will remain essential to align incentives, accelerate validation cycles, and ensure that ethical and privacy considerations are not sidelined in the pursuit of technological advancement. By attending to both the scientific and operational dimensions of AI integration, stakeholders can translate computational promise into robust, real-world genomic solutions.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Deep learning algorithms improving interpretation of rare genomic variants in clinical diagnostics
• 5.2. AI driven drug discovery platforms leveraging large scale genomic datasets for target identification
• 5.3. Integration of multi omics profiles with machine learning models for personalized treatment optimization
• 5.4. Federated learning frameworks enabling secure cross institution genomic data analysis without data sharing
• 5.5. Explainable AI solutions providing transparent genomic variant classification for regulatory compliance
• 5.6. AI powered single cell sequencing analytics to uncover cellular heterogeneity in complex diseases
• 5.7. Cloud based AI driven genomics pipelines offering scalable infrastructure for high throughput sequencing
• 5.8. Predictive genomics models using polygenic risk scoring to forecast disease susceptibility in populations
• 5.9. Application of deep reinforcement learning to optimize CRISPR Cas9 off target prediction in gene editing
• 5.10. Real world evidence generation through AI enhanced mining of genomic and clinical health record data

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Artificial Intelligence in Genomics Market, by Application

• 8.1. Agriculture & Animal Genomics
  • 8.1.1. Crop Improvement
  • 8.1.2. Livestock Breeding
• 8.2. Diagnostics
  • 8.2.1. Clinical Diagnostics
  • 8.2.2. Research Diagnostics
• 8.3. Drug Discovery
  • 8.3.1. Lead Identification
  • 8.3.2. Preclinical Testing
  • 8.3.3. Target Validation
• 8.4. Precision Medicine
  • 8.4.1. Companion Diagnostics
  • 8.4.2. Personalized Therapeutics
  • 8.4.3. Pharmacogenomics

9. Artificial Intelligence in Genomics Market, by AI Technique

• 9.1. Deep Learning
  • 9.1.1. Autoencoders
  • 9.1.2. Convolutional Neural Networks
  • 9.1.3. Recurrent Neural Networks
• 9.2. Machine Learning
  • 9.2.1. Reinforcement Learning
  • 9.2.2. Supervised Learning
  • 9.2.3. Unsupervised Learning
• 9.3. Natural Language Processing
  • 9.3.1. Sentiment Analysis
  • 9.3.2. Text Mining

10. Artificial Intelligence in Genomics Market, by Service

• 10.1. Bioinformatics Services
  • 10.1.1. Annotation
  • 10.1.2. Data Analysis
  • 10.1.3. Interpretation
• 10.2. Consulting
  • 10.2.1. Implementation Support
  • 10.2.2. Strategy Development
• 10.3. Sequencing Services
  • 10.3.1. Exome Sequencing
  • 10.3.2. Transcriptome Sequencing
  • 10.3.3. Whole Genome Sequencing
• 10.4. Software & Platform
  • 10.4.1. Cloud-Based
  • 10.4.2. On-Premise

11. Artificial Intelligence in Genomics Market, by Sequencing Type

• 11.1. Next Generation Sequencing
  • 11.1.1. Illumina
  • 11.1.2. Ion Torrent
  • 11.1.3. PacBio
• 11.2. Sanger Sequencing
  • 11.2.1. Capillary
  • 11.2.2. Fluorescence

12. Artificial Intelligence in Genomics Market, by End User

• 12.1. Academic & Research
  • 12.1.1. Research Institutes
  • 12.1.2. Universities
• 12.2. Hospitals & Clinics
  • 12.2.1. Diagnostic Laboratories
  • 12.2.2. Medical Centers
• 12.3. Pharma & Biotech
  • 12.3.1. Biotech Firms
  • 12.3.2. Large Pharma

13. Artificial Intelligence in Genomics Market, by Region

• 13.1. Americas
  • 13.1.1. North America
  • 13.1.2. Latin America
• 13.2. Europe, Middle East & Africa
  • 13.2.1. Europe
  • 13.2.2. Middle East
  • 13.2.3. Africa
• 13.3. Asia-Pacific

14. Artificial Intelligence in Genomics Market, by Group

• 14.1. ASEAN
• 14.2. GCC
• 14.3. European Union
• 14.4. BRICS
• 14.5. G7
• 14.6. NATO

15. Artificial Intelligence in Genomics Market, by Country

• 15.1. United States
• 15.2. Canada
• 15.3. Mexico
• 15.4. Brazil
• 15.5. United Kingdom
• 15.6. Germany
• 15.7. France
• 15.8. Russia
• 15.9. Italy
• 15.10. Spain
• 15.11. China
• 15.12. India
• 15.13. Japan
• 15.14. Australia
• 15.15. South Korea

16. Competitive Landscape

• 16.1. Market Share Analysis, 2024
• 16.2. FPNV Positioning Matrix, 2024
• 16.3. Competitive Analysis
  • 16.3.1. F. Hoffmann-La Roche Ltd
  • 16.3.2. Thermo Fisher Scientific Inc.
  • 16.3.3. Illumina, Inc.
  • 16.3.4. Agilent Technologies, Inc.
  • 16.3.5. QIAGEN N.V.
  • 16.3.6. Bio-Rad Laboratories, Inc.
  • 16.3.7. PerkinElmer, Inc.
  • 16.3.8. BGI Genomics Co., Ltd
  • 16.3.9. NanoString Technologies, Inc.
  • 16.3.10. SOPHiA Genetics SA

LIST OF FIGURES

• FIGURE 1. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, 2018-2032 (USD MILLION)
• FIGURE 2. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY APPLICATION, 2024 VS 2032 (%)
• FIGURE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY APPLICATION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AI TECHNIQUE, 2024 VS 2032 (%)
• FIGURE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AI TECHNIQUE, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SERVICE, 2024 VS 2032 (%)
• FIGURE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SERVICE, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING TYPE, 2024 VS 2032 (%)
• FIGURE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING TYPE, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY END USER, 2024 VS 2032 (%)
• FIGURE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY END USER, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY REGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 13. AMERICAS ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 14. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 15. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 16. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 17. EUROPE ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 18. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 19. AFRICA ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 20. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY GROUP, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 22. ASEAN ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 23. GCC ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 24. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 25. BRICS ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 26. G7 ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 27. NATO ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 29. ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SHARE, BY KEY PLAYER, 2024
• FIGURE 30. ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET, FPNV POSITIONING MATRIX, 2024

LIST OF TABLES

• TABLE 1. ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SEGMENTATION & COVERAGE
• TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2024
• TABLE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, 2018-2024 (USD MILLION)
• TABLE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, 2025-2032 (USD MILLION)
• TABLE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY APPLICATION, 2018-2024 (USD MILLION)
• TABLE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY APPLICATION, 2025-2032 (USD MILLION)
• TABLE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, 2018-2024 (USD MILLION)
• TABLE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, 2025-2032 (USD MILLION)
• TABLE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AGRICULTURE & ANIMAL GENOMICS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 15. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CROP IMPROVEMENT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 16. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CROP IMPROVEMENT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 17. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CROP IMPROVEMENT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 18. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CROP IMPROVEMENT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 19. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CROP IMPROVEMENT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 20. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CROP IMPROVEMENT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LIVESTOCK BREEDING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 22. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LIVESTOCK BREEDING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LIVESTOCK BREEDING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 24. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LIVESTOCK BREEDING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 25. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LIVESTOCK BREEDING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 26. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LIVESTOCK BREEDING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 27. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, 2018-2024 (USD MILLION)
• TABLE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, 2025-2032 (USD MILLION)
• TABLE 29. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 31. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 32. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 33. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 34. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DIAGNOSTICS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 35. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLINICAL DIAGNOSTICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 36. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLINICAL DIAGNOSTICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 37. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLINICAL DIAGNOSTICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 38. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLINICAL DIAGNOSTICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 39. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLINICAL DIAGNOSTICS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 40. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLINICAL DIAGNOSTICS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 41. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RESEARCH DIAGNOSTICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 42. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RESEARCH DIAGNOSTICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 43. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RESEARCH DIAGNOSTICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 44. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RESEARCH DIAGNOSTICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 45. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RESEARCH DIAGNOSTICS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 46. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RESEARCH DIAGNOSTICS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 47. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, 2018-2024 (USD MILLION)
• TABLE 48. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, 2025-2032 (USD MILLION)
• TABLE 49. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 50. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 51. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 52. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 53. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 54. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DRUG DISCOVERY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 55. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LEAD IDENTIFICATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 56. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LEAD IDENTIFICATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 57. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LEAD IDENTIFICATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 58. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LEAD IDENTIFICATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 59. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LEAD IDENTIFICATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 60. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY LEAD IDENTIFICATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 61. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECLINICAL TESTING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 62. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECLINICAL TESTING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 63. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECLINICAL TESTING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 64. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECLINICAL TESTING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 65. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECLINICAL TESTING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 66. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECLINICAL TESTING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 67. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TARGET VALIDATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 68. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TARGET VALIDATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 69. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TARGET VALIDATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 70. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TARGET VALIDATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 71. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TARGET VALIDATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 72. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TARGET VALIDATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 73. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, 2018-2024 (USD MILLION)
• TABLE 74. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, 2025-2032 (USD MILLION)
• TABLE 75. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 76. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 77. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 78. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 79. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 80. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PRECISION MEDICINE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 81. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COMPANION DIAGNOSTICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 82. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COMPANION DIAGNOSTICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 83. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COMPANION DIAGNOSTICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 84. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COMPANION DIAGNOSTICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 85. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COMPANION DIAGNOSTICS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 86. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY COMPANION DIAGNOSTICS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 87. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PERSONALIZED THERAPEUTICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 88. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PERSONALIZED THERAPEUTICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 89. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PERSONALIZED THERAPEUTICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 90. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PERSONALIZED THERAPEUTICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 91. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PERSONALIZED THERAPEUTICS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 92. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PERSONALIZED THERAPEUTICS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 93. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PHARMACOGENOMICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 94. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PHARMACOGENOMICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 95. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PHARMACOGENOMICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 96. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PHARMACOGENOMICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 97. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PHARMACOGENOMICS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 98. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY PHARMACOGENOMICS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 99. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AI TECHNIQUE, 2018-2024 (USD MILLION)
• TABLE 100. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AI TECHNIQUE, 2025-2032 (USD MILLION)
• TABLE 101. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, 2018-2024 (USD MILLION)
• TABLE 102. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, 2025-2032 (USD MILLION)
• TABLE 103. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 104. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 105. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 106. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 107. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 108. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 109. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AUTOENCODERS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 110. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AUTOENCODERS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 111. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AUTOENCODERS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 112. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AUTOENCODERS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 113. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AUTOENCODERS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 114. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY AUTOENCODERS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 115. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 116. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 117. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 118. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 119. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 120. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 121. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 122. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 123. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 124. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 125. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 126. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 127. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, 2018-2024 (USD MILLION)
• TABLE 128. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, 2025-2032 (USD MILLION)
• TABLE 129. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 130. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 131. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 132. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 133. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 134. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 135. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY REINFORCEMENT LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 136. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY REINFORCEMENT LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 137. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY REINFORCEMENT LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 138. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY REINFORCEMENT LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 139. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY REINFORCEMENT LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 140. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY REINFORCEMENT LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 141. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUPERVISED LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 142. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUPERVISED LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 143. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUPERVISED LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 144. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUPERVISED LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 145. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUPERVISED LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 146. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SUPERVISED LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 147. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY UNSUPERVISED LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 148. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY UNSUPERVISED LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 149. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY UNSUPERVISED LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 150. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY UNSUPERVISED LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 151. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY UNSUPERVISED LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 152. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY UNSUPERVISED LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 153. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2024 (USD MILLION)
• TABLE 154. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2025-2032 (USD MILLION)
• TABLE 155. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 156. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 157. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 158. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 159. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 160. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 161. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SENTIMENT ANALYSIS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 162. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SENTIMENT ANALYSIS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 163. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SENTIMENT ANALYSIS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 164. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SENTIMENT ANALYSIS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 165. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SENTIMENT ANALYSIS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 166. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SENTIMENT ANALYSIS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 167. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TEXT MINING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 168. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TEXT MINING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 169. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TEXT MINING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 170. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TEXT MINING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 171. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TEXT MINING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 172. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TEXT MINING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 173. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SERVICE, 2018-2024 (USD MILLION)
• TABLE 174. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SERVICE, 2025-2032 (USD MILLION)
• TABLE 175. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, 2018-2024 (USD MILLION)
• TABLE 176. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, 2025-2032 (USD MILLION)
• TABLE 177. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 178. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 179. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 180. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 181. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 182. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY BIOINFORMATICS SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 183. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ANNOTATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 184. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ANNOTATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 185. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ANNOTATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 186. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ANNOTATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 187. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ANNOTATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 188. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ANNOTATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 189. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DATA ANALYSIS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 190. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DATA ANALYSIS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 191. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DATA ANALYSIS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 192. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DATA ANALYSIS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 193. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DATA ANALYSIS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 194. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY DATA ANALYSIS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 195. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY INTERPRETATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 196. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY INTERPRETATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 197. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY INTERPRETATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 198. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY INTERPRETATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 199. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY INTERPRETATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 200. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY INTERPRETATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 201. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, 2018-2024 (USD MILLION)
• TABLE 202. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, 2025-2032 (USD MILLION)
• TABLE 203. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 204. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 205. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 206. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 207. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 208. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CONSULTING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 209. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY IMPLEMENTATION SUPPORT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 210. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY IMPLEMENTATION SUPPORT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 211. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY IMPLEMENTATION SUPPORT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 212. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY IMPLEMENTATION SUPPORT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 213. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY IMPLEMENTATION SUPPORT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 214. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY IMPLEMENTATION SUPPORT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 215. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY STRATEGY DEVELOPMENT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 216. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY STRATEGY DEVELOPMENT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 217. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY STRATEGY DEVELOPMENT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 218. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY STRATEGY DEVELOPMENT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 219. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY STRATEGY DEVELOPMENT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 220. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY STRATEGY DEVELOPMENT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 221. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, 2018-2024 (USD MILLION)
• TABLE 222. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, 2025-2032 (USD MILLION)
• TABLE 223. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 224. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 225. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 226. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 227. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 228. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 229. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY EXOME SEQUENCING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 230. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY EXOME SEQUENCING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 231. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY EXOME SEQUENCING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 232. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY EXOME SEQUENCING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 233. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY EXOME SEQUENCING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 234. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY EXOME SEQUENCING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 235. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TRANSCRIPTOME SEQUENCING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 236. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TRANSCRIPTOME SEQUENCING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 237. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TRANSCRIPTOME SEQUENCING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 238. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TRANSCRIPTOME SEQUENCING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 239. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TRANSCRIPTOME SEQUENCING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 240. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY TRANSCRIPTOME SEQUENCING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 241. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY WHOLE GENOME SEQUENCING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 242. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY WHOLE GENOME SEQUENCING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 243. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY WHOLE GENOME SEQUENCING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 244. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY WHOLE GENOME SEQUENCING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 245. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY WHOLE GENOME SEQUENCING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 246. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY WHOLE GENOME SEQUENCING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 247. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, 2018-2024 (USD MILLION)
• TABLE 248. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, 2025-2032 (USD MILLION)
• TABLE 249. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, BY REGION, 2018-2024 (USD MILLION)
• TABLE 250. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, BY REGION, 2025-2032 (USD MILLION)
• TABLE 251. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 252. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 253. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 254. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SOFTWARE & PLATFORM, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 255. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLOUD-BASED, BY REGION, 2018-2024 (USD MILLION)
• TABLE 256. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLOUD-BASED, BY REGION, 2025-2032 (USD MILLION)
• TABLE 257. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLOUD-BASED, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 258. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLOUD-BASED, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 259. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLOUD-BASED, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 260. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY CLOUD-BASED, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 261. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ON-PREMISE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 262. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ON-PREMISE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 263. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ON-PREMISE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 264. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ON-PREMISE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 265. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ON-PREMISE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 266. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY ON-PREMISE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 267. GLOBAL ARTIFICIAL INTELLIGENCE IN GENOMICS MARKET SIZE, BY SEQUENCING TYPE, 2018-2024 (USD MILLION)

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