生物
间质细胞
计算生物学
细胞
转移
癌细胞
计算机科学
癌症
癌症研究
遗传学
作者
Aarthi Venkat,Scott E. Youlten,Beatriz P. San Juan,Carley A. Purcell,Shabarni Gupta,Matthew Amodio,Daniel Neumann,John G. Lock,Anton E. Westacott,Cerys S. McCool,Daniel B. Burkhardt,Andrew Benz,Annelie Mollbrink,Joakim Lundeberg,David van Dijk,Jeff Holst,Leonard D. Goldstein,Sarah Kummerfeld,Smita Krishnaswamy,Christine L. Chaffer
出处
期刊:Cancer Discovery
[American Association for Cancer Research]
日期:2025-06-24
卷期号:15 (10): 2139-2165
被引量:2
标识
DOI:10.1158/2159-8290.cd-24-0684
摘要
Defining critical cell states among cells that reside along a phenotypic continuum is a current biological and computational challenge. In this study, we present AAnet, a neural network that learns archetypal cell states of cancer cells. AAnet defines discrete spatially localized ATs that resolve intratumoral heterogeneity.
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