今天分享的是：沙利文：2024年AI生命组学市场研究报告 报告共计：18页 一、行业定义与核心特征 AI生命组学以多组学数据为基础，通过AI算法实现数据的高效管理与挖掘、多维度整合分析及疾病机理解析。其关键特征包 括： - 数据智能处理：针对临床队列数据的高维度、高噪声特性，AI技术可自动完成降维、去噪和特征选择，优化专病队列设计，加 速生物标志物发现。 - 多组学整合分析：整合DNA测序、RNA表达谱、代谢物谱等多源数据，通过算法融合不同层面信息，揭示基因调控网络与代 谢途径的动态关联。 - 疾病与药物研究赋能：在致病机理研究中，AI通过对比患者与健康个体数据识别分子标志物；在药物开发中，支持靶点发现、 抗体优化及虚拟筛选，显著缩短研发周期。 - 个性化医疗应用：基于患者组学数据与临床病史，AI提供定制化治疗方案，覆盖肿瘤免疫疗法、罕见病诊断等领域，提升治疗 精准度。 二、市场分类与产业链布局 AI生命组学市场分为五大核心领域： 1. AI队列数据中心解决方案：智能化管理临床数据与样本，优化患者招募和试验设计，提升研究效率。 《2024年AI生命组学市场研究报告》核心内容总结 AI生命组学作为生命科学与人工 ...

Core Insights - The report highlights the rapid growth of the AI genomics market, which is projected to expand from 16.4 billion yuan in 2020 to 70.3 billion yuan by 2028, with a compound annual growth rate (CAGR) of 24.79% from 2020 to 2023 and an expected CAGR of 17.12% from 2023 to 2028 [1][27][28]. Market Overview - AI genomics integrates artificial intelligence with life sciences, focusing on the analysis of multi-omics data such as genomics, transcriptomics, and proteomics to advance disease mechanism research, drug development, and personalized medicine [1][5]. - The market has evolved through several stages: the initial phase of genomics (2000-2010), the expansion of proteomics (2010-2020), the integration of multi-omics (2020-2023), and the current growth phase (2023-present) [17][18]. Key Applications - Core applications include AI cohort data centers, AI-BT software platforms, multi-omics data analysis, medical-engineering translation, and AI medical technology services [1][13][14]. - AI cohort data centers enhance clinical trial processes by optimizing patient recruitment and managing clinical data effectively [31]. - AI-BT software platforms streamline biobanking and laboratory information management, improving data handling and compliance [37][38]. Industry Drivers - The growth of the AI genomics market is driven by the demand for precision medicine, cost pressures in drug development, policy support (e.g., "Healthy China 2030"), and advancements in technology such as cloud computing and deep learning [2][27]. - The COVID-19 pandemic has accelerated the focus on life sciences technologies and clinical data collection, highlighting the importance of genomics in public health [27]. Challenges and Opportunities - The industry faces challenges such as data heterogeneity and insufficient cross-institutional collaboration [2]. - Future opportunities lie in vaccine development, veterinary and traditional Chinese medicine research, microbiome applications, and clinical diagnostics [2][23]. Data Integration and Analysis - AI genomics excels in integrating and analyzing diverse multi-omics data, addressing issues of data complexity and heterogeneity [6][42]. - The use of AI in disease mechanism research allows for the identification of key molecules and pathways associated with diseases, facilitating targeted therapies [7][23]. Drug Development - AI genomics provides revolutionary tools for drug discovery, optimization, and development, enhancing the efficiency of identifying drug targets and predicting drug interactions [8][51]. - The integration of AI in drug development processes helps reduce timelines and costs while improving the success rates of new therapies [51][52]. Personalized Medicine - AI genomics supports personalized medicine by analyzing patient-specific omics data to tailor treatment plans, improving therapeutic outcomes [9][57]. - The technology enables precise identification of disease subtypes, guiding treatment decisions and minimizing adverse effects [9][62]. Industry Ecosystem - The AI genomics ecosystem includes various stakeholders such as pharmaceutical companies, hospitals, academic institutions, data management providers, and AI technology firms, all contributing to the advancement of healthcare [58][60]. - Collaboration among these entities is crucial for leveraging AI capabilities to enhance drug development and clinical applications [58][62].