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Decoding tumor microenvironments through artificial tumor transcriptomes
Cancer Cell ( IF 50.3 ) Pub Date : 2022-08-08 , DOI: 10.1016/j.ccell.2022.07.008
Liqing Tian 1 , Jinghui Zhang 1
Affiliation  

In this issue of Cancer Cell, Zaitsev et al. (2022) present a machine-learning-based approach, trained from millions of artificial transcriptomes with admixed cell populations, for reconstructing tumor microenvironments (TMEs). The high accuracy of this approach, demonstrated through extensive validation, enables systematic investigation of TMEs in both research and clinical settings.



中文翻译:

通过人工肿瘤转录组解码肿瘤微环境

在本期《癌细胞》中,Zaitsev 等人。(2022) 提出了一种基于机器学习的方法,通过数百万个具有混合细胞群的人工转录组进行训练,用于重建肿瘤微环境 (TME)。通过广泛的验证证明,这种方法具有很高的准确性,可以在研究和临床环境中对 TME 进行系统研究。

更新日期:2022-08-09
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