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利用高分辨率的OMIC数据来预测免疫检查点抑制剂的反应和不良事件

Angelo Limeta¹, Francesco Gatto^1,2, Markus J Herrgård³

¹Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden.

Computational and structural biotechnology journal

|August 21, 2023

概括

借助人工智能利用omics数据可以预测患者对癌症免疫疗法的反应和不良事件. 这种方法提高了临床试验的成功率,并个性化治疗以获得更好的结果.

科学领域:

生物医学是生物医学.
计算生物学计算生物学
精准医学是一门精准的医学.

背景情况:

临床试验的成功受到不可预测的患者反应和不良事件的阻碍.
欧米克斯数据提供了对影响药物疗效的宿主,疾病和环境因素的见解.
人工智能 (AI) 对于分析大型omics数据集至关重要.

研究的目的:

讨论使用omics数据来预测癌症免疫疗法治疗结果的挑战.
探索针对个性化医疗和伴侣治疗的AI应用.
突出OMIC数据在疾病分析和临床决策中的潜力.

主要方法:

分析大规模的奥米克数据 (基因组学,转录组学等). ) 的情况.
人工智能和机器学习算法的应用.
对预测药物反应和不良事件的方法的审查.

主要成果:

与人工智能相结合的Omics数据可以改善患者选择免疫检查点抑制剂 (ICI) 等治疗方法.
通过人工智能驱动的奥米克分析来促进对伴侣疗法的可操作目标的识别.
预测模型显示,提高癌症免疫疗法的可翻译性是有前途的.

结论:

利用omics数据提出了挑战,但为预测癌症免疫治疗结果提供了重大潜力.
人工智能驱动的OMIC分析是推进个性化医疗和改善患者分层的关键.
基于Omics的方法广泛适用于癌症免疫疗法以外的各种复杂疾病.

关键词:

生物标志物生物标志物与免疫相关的不良事件免疫检查点抑制剂免疫检查点抑制剂俄米克斯 (Omics) 是一个电子游戏.预测模型的预测模型.

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