Article ; Online: Decoding tumor microenvironments through artificial tumor transcriptomes.
2022 Volume 40, Issue 8, Page(s) 809–811
Abstract: 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 ... ...
Abstract | 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. |
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MeSH term(s) | Humans ; Machine Learning ; Neoplasms/genetics ; Transcriptome ; Tumor Microenvironment/genetics |
Language | English |
Publishing date | 2022-08-10 |
Publishing country | United States |
Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment |
ZDB-ID | 2078448-X |
ISSN | 1878-3686 ; 1535-6108 |
ISSN (online) | 1878-3686 |
ISSN | 1535-6108 |
DOI | 10.1016/j.ccell.2022.07.008 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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