Decoding tumor microenvironments through artificial tumor transcriptomes.
Decoding tumor microenvironments through artificial tumor transcriptomes.
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通过人工肿瘤转录组解码肿瘤微环境。
DOI:
10.1016/j.ccell.2022.07.008
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发表时间:
2022-08-08
期刊:
影响因子:
50.3
通讯作者:
Zhang, Jinghui
中科院分区:
文献类型:
--
作者:
Tian, Liqing;Zhang, Jinghui
In this issue of Cancer Cell, Zaitsev et al present a machine-learning based approach, trained from millions of artificial transcriptomes with admixed cell populations, for reconstructing tumor microenvironment (TME). The high accuracy of this approach, demonstrated through extensive validation, enables systematic investigation of TME in both research and clinical setting.
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影响因子:
8.8
作者:
Saltz J;Gupta R;Hou L;Kurc T;Singh P;Nguyen V;Samaras D;Shroyer KR;Zhao T;Batiste R;Van Arnam J;Cancer Genome Atlas Research Network;Shmulevich I;Rao AUK;Lazar AJ;Sharma A;Thorsson V
通讯作者:
Thorsson V
影响因子:
30.8
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通讯作者:
Moore, Helen F.
影响因子:
50.3
作者:
Zaitsev, Aleksandr;Chelushkin, Maksim;Bagaev, Alexander
通讯作者:
Bagaev, Alexander
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通讯作者:
Alizadeh AA
影响因子:
13.6
作者:
Menden, Kevin;Marouf, Mohamed;Bonn, Stefan
通讯作者:
Bonn, Stefan