Predicting Breast Cancer Prognosis Derived from γδ T Cell Gene Signatures

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Original language	English
Journal	Artificial Intelligence in Health
DOIs	https://doi.org/10.36922/AIH025470105
Publication status	Published - 3 Feb 2026

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This output contributes to the following UN Sustainable Development Goals (SDGs)

@article{b45e1e4dad744ae492e692a45f28cfe4,

title = "Predicting Breast Cancer Prognosis Derived from γδ T Cell Gene Signatures",

author = "Jia Weng and Jicheng Weng and Antony Kam and Shining Loo and Lina Zhou and Rencai Fan and Runwei Guan and Shicheng Li and Kai Chen",

year = "2026",

month = feb,

day = "3",

doi = "10.36922/AIH025470105",

language = "English",

journal = "Artificial Intelligence in Health",

issn = "3029-2387",

}

TY - JOUR

T1 - Predicting Breast Cancer Prognosis Derived from γδ T Cell Gene Signatures

AU - Weng, Jia

AU - Weng, Jicheng

AU - Kam, Antony

AU - Loo, Shining

AU - Zhou, Lina

AU - Fan, Rencai

AU - Guan, Runwei

AU - Li, Shicheng

AU - Chen, Kai

PY - 2026/2/3

Y1 - 2026/2/3

U2 - 10.36922/AIH025470105

DO - 10.36922/AIH025470105

M3 - Article

SN - 3029-2387

JO - Artificial Intelligence in Health

JF - Artificial Intelligence in Health

ER -