在数字病理学和空间奥米克学中量化复杂性的透量度
Xiao Li1, Xuehan Ren2, Raghavan Venugopal1
1Computational Science and Informatics, Roche Diagnostics Solutions, Santa Clara, CA, USA.
iScience
|June 23, 2025
概括
值量化了生物系统的复杂性. 本综述详细介绍了其在数字病理学和空间奥米学中的应用,用于组织分析,生物标志物发现和推进精准医学.
科学领域:
- 信息理论是信息理论.
- 计算生物学是一种计算生物学.
- 数字病理学数字病理学
背景情况:
- 度生物系统中的混乱,异质性和复杂性.
- 它是信息理论中的一个基本概念,具有广泛的应用.
- 了解组织复杂性对于疾病诊断和治疗至关重要.
研究的目的:
- 审查在数字病理学和空间空间学中的应用.
- 探索如何量化组织架构,空间异质性和细胞组织.
- 突出在生物标志物发现和精准医学中的潜力.
主要方法:
- 合成的理论基础,包括古典和空间意识的指标.
- 检查组织异质性测绘和微环境分析中的应用.
- 讨论AI/ML集成用于基于的分析.
主要成果:
- 能有效地捕捉复杂的组织特征和细胞组织.
- 基于的方法有助于识别组织异质性和微环境概况.
- 人工智能/ML方法提高了在生物标志物发现中的实用性.
结论:
- 基于的方法为在数字病理学和空间奥米克学中分析生物系统提供了强大的工具.
- 未来的方向包括基于图的,动态和整合性数据方法.
- 这些方法有望推进精准医学和个性化治疗策略.
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