Tri-Clustering Analysis for Dissecting Epigenetic Patterns Across Multiple Cancer Types

被引:2
作者
Gan, Yanglan [1 ]
Dong, Zhiyuan [1 ]
Zhang, Xia [2 ]
Zou, Guobing [2 ]
机构
[1] Donghua Univ, Sch Comp Sci & Technol, Shanghai, Peoples R China
[2] Shanghai Univ, Sch Comp Engn & Sci, Shanghai, Peoples R China
来源
INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT II | 2018年 / 10955卷
基金
中国国家自然科学基金;
关键词
Tri-clustering; Epigenetic pattern; Pan-cancer; EPIGENOME; DYNAMICS; GASC1; GENE;
D O I
10.1007/978-3-319-95933-7_40
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Tumor cells not only harbor genetic and epigenetic alterations, but also are regulated by various epigenetic modifications. Identification of tumor epigenetic similarities across different cancer types is useful for the discovery of treatments that can be extended to different cancers. Nowadays, abundant epigenetic modification profiles have provided good opportunity to achieve this goal. Here, we proposed a tri-clustering approach for integrative pan-cancer epigenomic analysis, named TriPCE. We applied TriPCE to uncover epigenetic mode among seven cancer types. This approach can identify significant cross-cancer epigenetic modification similarities. The associated gene analysis demonstrates strong relevance with cancer development and reveals consistent tendency among cancer types.
引用
收藏
页码:330 / 336
页数:7
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