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Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Authors :
David J. Klinke II
Audry Fernandez
Wentao Deng
Atefeh Razazan
Habibolla Latifizadeh
Anika C. Pirkey
Source :
Nature Communications, Vol 13, Iss 1, Pp 1-15 (2022)
Publication Year :
2022
Publisher :
Nature Portfolio, 2022.

Abstract

While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.56b0dfcecb344268bc44ce5dfb946875
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-022-29636-3