以单细胞临床数据为基础的基于变压器的人工智能,用于同居机制推断和理性生物标志物发现
Veronica Tozzo1,2,3, Lily H Zhang4, Rajesh Ranganath4,5
1Department of Pathology and Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA.
medRxiv : the preprint server for health sciences
|April 8, 2025
概括
应用于单细胞数据的人工智能 (AI) 揭示了复杂的血细胞动态. 我们的人工智能管道准确预测血细胞计数,并发现具有临床相关性的新生物标志物.
科学领域:
- 计算生物学 计算生物学
- 血液学 血液学 血液学
- 人工智能的人工智能
背景情况:
- 单细胞数据分析为理解生物系统提供了变革性的潜力.
- 传统的方法往往错过了复杂的生物数据中的复杂模式和机制.
研究的目的:
- 开发和应用一种通用,可解释的AI管道,用于分析单细胞血液学数据.
- 揭示血细胞生产和清除中的种群流动和恒温机制.
- 为了确定新的生物标志物,用于诸如败血症,心脏病和糖尿病等疾病.
主要方法:
- 开发两种深度学习模型管道:用于预测的多输入集变压器++ (MIST) 和用于解释性的单细胞FastShap.
- 应用到一大数据集的常规临床单细胞测量红细胞 (RBC),白细胞 (WBC) 和血小板 (PLT).
- 使用可解释性地图来识别关键的单细胞子组,并为共同监管机制提出假设.
主要成果:
- MIST模型解释了血液细胞群大小变化的70-82%,明显优于目前的方法 (5-20%).
- 在RBC,WBC和PLT种群中确定了实质性的交叉和共同调节关系.
- 发现了一种新的单个WBC生物标志物"下移",增强了与炎症疾病的诊断关联.
结论:
- 开发的AI管道有效地利用单细胞数据进行具有显著临床相关性的机械推理.
- 可解释的人工智能模型可以通过从复杂的生物数据中生成可测试的假设来加速科学发现.
- 这种方法证明了人工智能在发现隐藏的血液学机制和识别新的诊断生物标志物的能力.
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