人工智能和翻译性癌症研究的交界处的新视野
Josephine Yates1, Eliezer M Van Allen2
1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland; ETH AI Center, ETH Zurich, Zurich, Switzerland; Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland.
Cancer cell
|April 15, 2025
概括
人工智能 (AI) 和多组学正在通过分析复杂的数据来发现目标和患者分层来彻底改变癌症研究. 将这些人工智能工具集成到临床实践中,有望推进精确瘤学和改善患者护理.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 人工智能 (AI) 是一种关键的计算策略,用于分析癌症研究中的多组数据集.
- 单细胞和空间分析技术使得人们对瘤生物学有了更深入的了解.
- 人工智能加速转化瘤学,包括目标发现,生物标记物识别,患者分层和预测治疗反应.
研究的目的:
- 审查AI应用在多组学分析和转化瘤学.
- 突出AI在推动生物发现和为临床决策提供信息方面的作用.
- 探索将AI整合到临床工作流程中的挑战和机遇.
主要方法:
- 综述当前关于人工智能在多态学和转化瘤学的应用的文献.
- 专注于人工智能在分析细胞异质性和瘤微环境方面的作用.
- 讨论人工智能辅助的诊断和治疗反应预测.
主要成果:
- 人工智能对于分析癌症中复杂的多组数据至关重要.
- 人工智能有助于识别潜在的药物点,生物标志物和患者子组.
- 人工智能工具在预测治疗结果和改善诊断方面表现有前途.
结论:
- 人工智能和多元经济学对提高精确瘤学的潜力很大.
- 应对可重现性和可解释性等挑战对于临床整合至关重要.
- 人工智能集成可以改善癌症护理和患者的治疗结果.
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