CellPhenoX:一种可解释的机器学习方法,用于识别细胞表型,以预测单细胞多组的临床结果
Jade Young1, Jun Inamo1,2, Zachary Caterer3
1Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|September 23, 2025
概括
一种新的可解释的人工智能方法CellPhenoX通过识别细胞特异性得分和相互作用效应,将细胞表型与临床结果联系起来. 这有助于从单细胞数据了解疾病异质性和临床影响.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 人工智能的人工智能是人工智能.
背景情况:
- 单细胞技术揭示了疾病异质性,但很难将细胞表型与临床结果联系起来.
- 现有的方法缺乏解释性,并与复杂的相互作用效应 (例如性别,年龄,疾病) 斗争.
研究的目的:
- 开发一种可解释的机器学习方法,CellPhenoX,用于识别与临床结果相关的细胞特异性表型和相互作用效应.
- 通过提供可解释的细胞特异性见解,提高单细胞数据的临床翻译.
主要方法:
- CellPhenoX集成了分类模型,可解释的人工智能 (AI) 和统计框架.
- 它产生可解释的,细胞特异性得分,以识别与疾病相关的细胞群.
- 该方法使用模拟和真实世界患者队列进行了基准测试.
主要成果:
- 在各种单细胞研究中,CellPhenoX成功地确定了细胞特异的表型和相互作用效应.
- 它检测到COVID-19中激活的单细胞表型与疾病严重程度相关.
- 该方法揭示了预测炎症的纤维细胞状态过渡,并确定了瘤微环境中的治疗诱导的T细胞变化.
结论:
- CellPhenoX提供了一个强大的,可解释的框架,用于将单细胞发现转化为临床影响.
- 该方法有效地解决了将细胞水平数据与临床结果和相互作用效应联系在一起的挑战.
- 它在理解疾病异质性和识别生物标志物方面具有广泛的适用性.
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