基因組學人工智慧市場：按應用、人工智慧技術、服務、序列類型和最終用戶分類-全球預測，2025-2032年

預計到 2032 年，基因組學領域的人工智慧市場將成長至 75.3014 億美元，複合年成長率為 33.63%。

關鍵市場統計數據
基準年 2024	7.4023億美元
預計年份：2025年	9.8496億美元
預測年份 2032	75.3014億美元
複合年成長率 (%)	33.63%

先進計算建模、高通量測序和數據工程的融合如何重新定義基因組學領域的發現、診斷和治療決策

人工智慧正透過將演算法的嚴謹性與生物學洞察力結合，迅速重塑基因組學，使以往難以實現的發現成為可能。模型架構的進步、註釋資料集的日益豐富以及雲端原生運算生態系統的構建，共同提升了基因組訊號解讀的速度和準確性。計算方法與高通量定序的融合，為理解遺傳變異、識別治療標靶以及將分子特徵轉化為臨床可操作的決策提供了新的途徑。

此外，在診斷領域，臨床和研究專用檢測中模式識別技術的進步正在縮短結果解讀等待時間。在藥物研發領域，計算模型正在簡化先導化合物的篩選流程，改善標靶檢驗，並提高臨床前試驗的效率。在精準醫療領域，預測演算法正在為伴隨診斷的開發提供訊息，塑造個人化治療策略，並支持藥物基因組學決策。

本導言執行摘要的其餘部分奠定了基礎，重點闡述了演算法創新、資料保真度和服務交付之間的相互作用。它強調，持續進步將取決於穩健的註釋和解讀實踐、跨定序平台的整合以及來自學術界、臨床機構和產業界的相關人員之間的合作。因此，領導者在將人工智慧融入基因組學工作流程時，必須同時考慮技術機會和操作複雜性。

新的演算法架構、整合的序列到分析工作流程以及協作模式加速了翻譯進程，同時合規性和驗證也成為更重要的優先事項。

基因組學領域正經歷著變革性的轉變，這主要得益於模型能力的提升、多模態資料集的豐富以及端到端運算流程的日趨成熟。深度學習架構，包括卷積神經網路和循環神經網路，如今已廣泛應用於需要空間模式識別和時間序列解釋的任務；而自編碼器則有助於降維和潛在表徵學習，從而揭示隱藏的生物學關係。機器學習範式，例如監督學習和無監督學習，仍然是分類和叢集任務的基礎；強化學習也開始在高通量環境下指導實驗設計和資源分配。應用於生物醫學文獻和臨床記錄的自然語言處理技術正在改善資訊搜尋並加速假設生成。

方法論的這種轉變與服務創新同步發生。生物資訊服務正朝著模組化和雲端整合的方向發展，使得註釋流程和解讀引擎能夠以可擴展服務的形式提供，而非客製化計劃。定序服務正日益與分析平台結合，從而使EXOME、轉錄組和全基因組的輸出能夠直接流入經過檢驗的計算工作流程。諮詢服務也正從單純的效能交付轉向涵蓋資料管治、模型檢驗和部署流程的策略夥伴關係關係。

在營運層面，學術機構、臨床實驗室和商業實體正朝著更整合的協作模式發展，透過存取控制機制共用。這種轉變減少了重複工作，加快了模型訓練速度，並提高了可重複性。同時，對可解釋性、溯源性和合規性日益成長的需求，推動了標準化本體、版本化流程和嚴格檢驗框架的採用。這些變革性趨勢不僅能夠更快地將實用化，也提高了品質保證和倫理管理的標準。

美國累積關稅措施對基因組研究供應鏈、籌資策略、合作研究實踐和國內創新重點的定性影響

2025年美國關稅政策的動態為基因組學領域的供應鏈、採購決策和研究合作帶來了一系列複雜的定性壓力。這些壓力的累積效應加劇了跨境採購的敏感性，促使研究機構重新審視供應商選擇標準，並評估試劑、儀器和計算設備供應管道的韌性。事實上，這已促使許多相關人員加快供應商多元化的步伐，並探索關鍵耗材和設備的本土化生產和區域製造夥伴關係。

同時，規模較小的實驗室和研究團隊正在探索聯合採購聯盟和替代籌資策略，以降低成本波動。這些措施正在重塑供應商關係，並將商業性對話的重點轉向整體擁有成本、前置作業時間保證和服務水準承諾。

研究合作和資料共用安排也在不斷調整。以往跨境計劃依賴快速的試劑補給和儀器服務契約，而現在團隊更加重視數據可移植性和遠端分析能力，將其作為應急機制。當實體元件因海關原因延誤時，雲端原生分析平台和軟體即服務（SaaS）已成為維持專案連續性的關鍵。同時，人們對智慧財產權和資料在地化的日益關注，也促使人們重新重視更嚴格的合約框架和本地監管合規性。

在創新方面，關稅壓力正促使國內投資於替代技術，包括定序耗材的生產、更易於在地採購的模組化儀器設計，以及減少對專有硬體依賴的軟體平台。雖然這些轉變並不能消除專業化和規模經濟帶來的權衡取捨，但它們正在重塑競爭格局，並鼓勵專注於提供具成本效益本土解決方案的新進業者。最終，2025年關稅的累積影響加速了供應鏈的策略性重新評估，提升了整合分析服務的價值，並提高了基因組學工作流程中營運韌性的重要性。

透過細分市場主導了解應用需求、演算法方法、服務模式、定序方式和最終用戶優先順序將如何決定基因組人工智慧的採用路徑。

透過精細的細分視角，我們可以了解基因組人工智慧在臨床、農業和商業領域中最具實際價值的應用方向。應用領域包括農業和動物基因組學，它們受益於演算法性狀選擇和基因組選擇方法，加速作物改良和牲畜育種，並使育種者能夠更有效地優先考慮產量、適應性和抗病性。診斷領域涵蓋臨床診斷實驗室和研究診斷團隊。人工智慧透過改進變異解讀和縮短週轉時間，對高通量檢測進行了補充。研究診斷利用模式發現來產生用於後續檢驗的假設。藥物發現將計算方法擴展到先導化合物識別、標靶驗證和臨床前檢驗，人工智慧模型增強了虛擬篩檢、預測脫靶效應並最佳化了實驗優先順序。精準醫療整合了伴隨診斷、個人化治療和藥物基因組學，以基於先導化合物和臨床數據相結合識別的預測性生物標記來客製化治療方案。

在人工智慧技術領域，深度學習的進步，包括自編碼器、卷積類神經網路和循環神經網路，對基於序列的模式識別和表徵學習產生了特別顯著的影響。機器學習的子領域，例如監督學習、無監督學習和強化學習，是分類、叢集和最佳化實驗策略的核心。應用於文獻挖掘和臨床文本的自然語言處理技術，能夠從非結構化資料來源中提取可操作的見解，從而促進快速的證據整理並支持轉化研究。

以服務為導向的細分強調了整合服務的重要性。生物資訊服務提供註釋、資料分析和解讀，是把原始序列轉化為可解釋結果的基礎。諮詢服務提供實施支援和策略制定，幫助企業將技術部署與臨床和商業性目標保持一致。定序服務涵蓋EXOME定序、轉錄組定序和全基因定序，為下游分析奠定基礎。軟體和平台的選擇（無論是雲端基礎還是本地部署）決定了可擴展性、資料管治和延遲情況。

定序方式的差異對分析流程和採購都至關重要。新一代定序平台，例如 Illumina、Ion Torrent 和 PacBio，提供多種讀長、通量和錯誤率，從而影響模型訓練和結果解讀策略。使用毛細管和螢光定序的 Sanger 定序仍然是驗證和標靶分析的重要方法。學術和研究機構，包括研究所和大學，優先考慮方法的開放性和可重複性。醫院和診所，包括診斷實驗室和醫療中心，優先考慮法規遵循、週轉時間和與臨床工作流程的整合。製藥和生物技術公司，包括生物技術公司和大型製藥公司，需要可擴展的流程、智慧財產權保護和符合監管標準的驗證，以支持藥物開發和伴隨診斷策略。

綜上所述，這些細分洞察表明，人工智慧在基因組學領域的成功部署需要對技術選擇、服務模式、定序方式和最終用戶優先順序的細緻協調。針對特定應用需求和營運限制量身定做的解決方案將獲得更高的採用率和更大的後續影響。

區域市場動態如何影響美洲、歐洲、中東和非洲以及亞太地區的基因組人工智慧商業化、監管和合作

地理動態對全球基因組學生態系統的投資模式、法規環境和合作研究有顯著影響。美洲地區持續展現出產業界、學術中心和臨床系統之間的緊密整合，擁有成熟的創投舉措支援轉化研究，以及強大的雲端基礎分析基礎設施。儘管這種環境有利於人工智慧工具的快速商業化，但也面臨日益嚴格的監管審查，以及對資料安全和病患知情同意框架日益成長的重視。

歐洲、中東和非洲地區（EMEA）的監管體系複雜多樣，各國在報銷和臨床應用方面既存在差異，也存在協調統一的努力。公共部門對基因組學和合作研究聯盟的投資是該地區的顯著特徵，該地區高度重視資料保護、倫理管治和互通性標準。這些優先事項正在影響供應商的策略，促使他們開發優先考慮隱私保護分析、透明溯源和符合當地衛生部門要求的解決方案。

亞太地區擁有高通量測序能力、部分市場強大的本土製造業，以及正在加速的官民合作關係項目，這些都推動了大規模基因組學計畫的實施。各國政府致力於利用基因組學實現國家健康和糧食安全目標，這促進了臨床基因組學和農業領域的快速發展。此外，該地區還擁有日益壯大的人工智慧人才和雲端基礎設施提供商生態系統，這些因素共同推動了本地化創新、加快了迭代周期，並為現有供應商提供了更具競爭力的替代方案。

在這些全部區域，跨境合作依然存在，但日益受到數據主權、供應鏈韌性和監管協調等因素的限制。能夠兼顧當地採購慣例、臨床檢驗要求以及數據使用方面的文化預期等因素的區域策略，在市場滲透和持續影響方面都將具有顯著優勢。

平台巨頭、專業設備製造商、雲端服務供應商和創新新興企業如何透過夥伴關係、檢驗和營運彈性來塑造競爭格​​局

人工智慧基因組學領域的競爭格局將由平台巨頭、專業儀器製造商、雲端處理供應商以及新興新新興企業共同構成，這些企業將各自在領域專業知識和創新演算法方法方面相結合。平台巨頭將提供包含定序、分析和支援服務的整合解決方案，而專業儀器製造商則專注於提升通量、準確性和耗材成本。雲端運算和運算提供者將支援可擴展的模型訓練和推理，從而降低缺乏大規模本地基礎設施的機構的准入門檻。

新興企業和專業供應商正透過新的模型架構、目標資料集和服務產品實現差異化，以解決特定的痛點，例如臨床級可解釋性、低資源部署以及用於分散式檢查的邊緣分析。儀器製造商和軟體供應商之間的夥伴關係日益普遍，這反映出業界對端到端檢驗解決方案的偏好，這些解決方案可以降低最終用戶的整合風險。學術衍生公司和聯盟主導的舉措繼續支持創新進程，它們通常與商業實體合作，以推進研究成果的檢驗和監管批准。

成功的公司將具備卓越的技術實力、可重複的檢驗機制、完善的資料管治實務以及清晰的終端用戶價值提案。投資於透明的模型文件、基於獨立資料集的嚴格基準測試以及與臨床或農業合作夥伴開展的合作試驗的公司，將更有利於克服推廣應用方面的障礙。同樣重要的是，要建立策略聯盟，以確保供應鏈的連續性和區域影響力。

為高階主管提供切實可行的、循序漸進的策略建議，以確保人工智慧在基因組學領域透過資料管治、供應鏈彈性、模組化架構和道德管理發揮價值。

產業領導者應採取務實、分階段的方法將人工智慧融入基因組學，在創新與營運嚴謹性之間取得平衡。首先，要明確與組織能力和監管限制相符的高影響力應用案例，然後優先投資於能夠在這些應用案例中帶來可複製價值的項目。早期工作應著重於建立健全的資料管理、溯源追蹤和標註標準，以確保模型基於可靠且文件齊全的資料集進行訓練。

研發領導者還應制定混合採購策略，透過結合區域供應商、關鍵耗材的長期合約以及雲端基礎的運算和分析容錯移轉方案來降低供應鏈風險。與學術中心和臨床實驗室建立策略夥伴關係可以加快檢驗，並提供獲取多樣化資料集的途徑。

從技術角度來看，應採用模組化架構，使團隊能夠在不中斷檢驗工作流程的情況下更換模型元件和序列輸入。應重視可解釋性和文件記錄，以促進監管審查和臨床醫生的認可，並投資於持續監測和部署後檢驗，以檢測模型漂移並維持其性能。最後，應將倫理管治和隱私保護技術融入專案設計，以建立與病患、監管機構和商業夥伴的信任。採取這些措施將有助於組織充分利用人工智慧的優勢，同時管控基因組學應用中固有的營運和聲譽風險。

混合方法研究框架，結合專家訪談、文獻綜述、獨立基準化分析、情境分析和倫理管治審查，以提供可靠且可操作的見解。

本分析的調查方法結合了定性專家訪談、技術文獻的系統性回顧以及相關人員訪談檢驗，以確保結論的全面性和平衡性。主要見解來自於與來自學術界、臨床診斷、設備製造和軟體開發等不同領域的專家的結構化對話。此外，還對同行評審的研究、技術預印本、監管指導文件和公開的產品規格進行了嚴格的審查，以確保技術評估是基於最新證據。

為確保分析的嚴謹性，我們採用交叉檢驗，將演算法聲明與獨立的基準資料集進行比對，並對報告的模型架構和效能指標進行可複現性檢查。我們結合採購案例、供應商文件和實際實施報告，對服務和商業化的洞察進行三角驗證，以捕捉現實世界的限制因素。此外，分析還包含基於情境的思考，以探索應對外部壓力（例如供應鏈中斷和不斷變化的監管預期）的營運回應措施。

調查方法明確納入了倫理和隱私方面的考量，包括評估資料管治框架、同意機制以及聯邦學習和安全飛地等隱私保護計算技術。研究的局限性和不確定領域也已記錄在案，以便讀者評估研究結果的實際適用性，並確定後續基礎研究和試點工作的優先事項。

將技術前景與營運需求相結合，以展示數據品質、互通性和協作驗證如何推動基因組檢驗在現實世界中產生影響。

人工智慧正在加速基因組學的變革，顯著提升了生物學假設的生成、檢驗和轉化速度及準確性。這項技術正推動更精準的農業育種、更快更準確的診斷、更簡化的藥物研發流程以及日益個人化的治療策略。然而，進步並非僅僅取決於模型的複雜程度；它同樣依賴資料品質、定序平台與分析工具之間的互通性，以及能夠應對監管和供應鏈波動的靈活運作模式。

展望未來，那些將技術嚴謹性與務實營運策略（例如強大的資料管理、模組化技術架構、區域資料來源多元化和透明的檢驗實踐）相結合的機構，將最有能力實現持續的影響力。學術界、臨床系統、產業界和政策制定者之間的合作仍然至關重要，這有助於協調獎勵、加快檢驗週期，並確保倫理和隱私方面的考量不會阻礙技術進步。專注於人工智慧整合的科學和營運層面，將使相關人員能夠把計算前景轉化為穩健的、實際的基因組解決方案。

目錄

第1章：序言

第2章調查方法

第3章執行摘要

第4章 市場概覽

第5章 市場洞察

• 深度學習演算法提高了臨床診斷中對罕見基因組變異的解讀能力。
• 利用大規模基因組資料集辨識標靶的AI驅動藥物發現平台
• 將多組體學分析與機器學習模型結合，實現個人化治療最佳化
• 一種聯邦學習框架，可在不共用資料的情況下實現安全的跨機構基因組資料分析。
• 可解釋人工智慧解決方案，提供透明的基因組變異分類，以滿足監管合規要求
• 人工智慧驅動的單細胞定序揭示複雜疾病中的細胞異質性
• 雲端基礎、人工智慧驅動的基因組學流程，為高通量測序提供可擴展的基礎設施
• 利用多基因風險評分的預測基因組學模型預測族群疾病感受性
• 應用深度強化學習最佳化 CRISPR Cas9 在基因編輯中的脫靶預測
• 利用人工智慧挖掘基因組和臨床健康記錄數據，以產生真實世界證據

第6章：美國關稅的累積影響，2025年

第7章：人工智慧的累積影響，2025年

8. 基因組學人工智慧市場（按應用領域分類）

• 農業與動物基因體學
  • 作物改良
  • 畜牧業
• 診斷
  • 臨床診斷
  • 調查和診斷
• 藥物發現
  • 鉛識別
  • 臨床前試驗
  • 目標檢驗
• 精準醫療
  • 伴隨診斷
  • 個人化治療
  • 藥物基因組學

9. 人工智慧技術在基因體學市場的應用

• 深度學習
  • 自編碼器
  • 卷積類神經網路
  • 循環神經網路
• 機器學習
  • 強化學習
  • 監督式學習
  • 無監督學習
• 自然語言處理
  • 情緒分析
  • 文字探勘

10. 基因組學人工智慧市場（按服務分類）

• 生物資訊服務
  • 評論
  • 數據分析
  • 解釋
• 諮詢
  • 實施支持
  • 策略發展
• 定序服務
  • EXOME定序
  • 轉錄組定序
  • 全基因測序
• 軟體和平台
  • 雲端基礎的
  • 本地部署

11. 基因組學人工智慧市場（按序列類型分類）

• 次世代定序
  • Illumina
  • 離子洪流
  • 包裝生物
• 桑格定序
  • 微血管
  • 螢光

第12章 基因組學人工智慧市場（按最終用戶分類）

• 學術研究
  • 研究所
  • 大學
• 醫院和診所
  • 診斷實驗室
  • 醫療中心
• 製藥和生物技術
  • 生技公司
  • 大型製藥公司

13. 各地區基因體學人工智慧市場

• 美洲
  • 北美洲
  • 拉丁美洲
• 歐洲、中東和非洲
  • 歐洲
  • 中東
  • 非洲
• 亞太地區

第14章 基因組學人工智慧市場（按群體分類）

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

15. 各國基因組學人工智慧市場

• 美國
• 加拿大
• 墨西哥
• 巴西
• 英國
• 德國
• 法國
• 俄羅斯
• 義大利
• 西班牙
• 中國
• 印度
• 日本
• 澳洲
• 韓國

第16章 競爭格局

• 2024年市佔率分析
• FPNV定位矩陣，2024
• 競爭分析
  • 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)

T