从单细胞细胞计数数据中发现免疫治疗生物标志物。
Discover immunotherapy biomarkers from single-cell cytometry data.
作者信息
Ru Beibei, Jiang Peng
机构信息
Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
出版信息
Patterns (N Y). 2021 Dec 10;2(12):100384. doi: 10.1016/j.patter.2021.100384.
Abstract
Currently, identifying novel biomarkers remains a crucial need for cancer immunotherapy. By leveraging single-cell cytometry data, Greene et al. developed an interpretable machine learning method, FAUST, to discover cell populations associated with clinical outcomes.
摘要
目前，识别新型生物标志物仍然是癌症免疫治疗的迫切需求。通过利用单细胞流式细胞术数据，格林等人开发了一种可解释的机器学习方法FAUST，以发现与临床结果相关的细胞群体。
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