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Inferring cancer common and specific gene networks via multi-layer joint graphical model

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成果类型:
期刊论文
作者:
Chen, Yuanxiao;Zhang, Xiao-Fei;Ou-Yang, Le
通讯作者:
Ou-Yang, L.
作者机构:
[Ou-Yang, Le; Chen, Yuanxiao] Shenzhen Univ, Guangdong Key Lab Intelligent Informat Proc, Shenzhen Key Lab Media Secur, Shenzhen, Peoples R China.
[Ou-Yang, Le; Chen, Yuanxiao] Shenzhen Univ, Guangdong Lab Artificial Intelligence & Digital Ec, Shenzhen, Peoples R China.
[Zhang, Xiao-Fei] Cent China Normal Univ, Sch Math & Stat, Wuhan, Peoples R China.
[Zhang, Xiao-Fei] Cent China Normal Univ, Hubei Key Lab Math Sci, Wuhan, Peoples R China.
通讯机构:
[Ou-Yang, L.] G
Guangdong Key Laboratory of Intelligent Information Processing, China
语种:
英文
关键词:
Gaussian graphical models;Gene network inference;Single-cell RNA sequencing;Sparse penalty
期刊:
Computational and Structural Biotechnology Journal
ISSN:
2001-0370
年:
2023
卷:
21
页码:
974-990
基金类别:
This work is supported in part by funds from the National Natural Science Foundation of China [ 62173235 , 61602309 , 12271198 , 11871026 ], Guangdong Basic and Applied Basic Research Foundation [ 2022A1515010146,2019A1515011384 ], and the (Key) Project of Department of Education of Guangdong Province [No. 2022ZDZX1022 ].
机构署名:
本校为其他机构
院系归属:
数学与统计学学院
摘要:
Cancer is a complex disease caused primarily by genetic variants. Reconstructing gene networks within tumors is essential for understanding the functional regulatory mechanisms of carcinogenesis. Advances in high-throughput sequencing technologies have provided tremendous opportunities for inferring gene networks via computational approaches. However, due to the heterogeneity of the same cancer type and the similarities between different cancer types, it remains a challenge to systematically investigate the commonalities and specificities between gene networks of different cancer types, which ...

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