DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data

DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data

Abstract Background Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and risk prediction models for prioritizing genet...

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Bibliographic Details
Main Authors: Jiaqi Liu, Hengqiang Zhao, Yu Zheng, Lin Dong, Sen Zhao, Yukuan Huang, Shengkai Huang, Tianyi Qian, Jiali Zou, Shu Liu, Jun Li, Zihui Yan, Yalun Li, Shuo Zhang, Xin Huang, Wenyan Wang, Yiqun Li, Jie Wang, Yue Ming, Xiaoxin Li, Zeyu Xing, Ling Qin, Zhengye Zhao, Ziqi Jia, Jiaxin Li, Gang Liu, Menglu Zhang, Kexin Feng, Jiang Wu, Jianguo Zhang, Yongxin Yang, Zhihong Wu, Zhihua Liu, Jianming Ying, Xin Wang, Jianzhong Su, Xiang Wang, Nan Wu
Format: Article
Language:English
Published: BMC 2022-02-01
Series:Genome Medicine
Subjects:
Hereditary breast cancer
Deep learning
BRCA1/2
Genetic test
Genotype-phenotype correlation
Online Access:https://doi.org/10.1186/s13073-022-01027-9

Internet

https://doi.org/10.1186/s13073-022-01027-9

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