文献详情
Identification of cancer omics commonality and difference via community fusion
文献类型：期刊
通讯作者：Ma, SG (reprint author), Yale Univ, Dept Biostat, 60 Coll St, New Haven, CT 06520 USA.
期刊名称：STATISTICS IN MEDICINE影响因子和分区
年：2019
卷：38
期：7
页码：1200-1212
ISSN：0277-6715
关键词：commonality and difference; community fusion; multi-cancer analysis; network-based analysis
所属部门：统计学院
摘要：The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approac ...More
The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings. ...Hide

DOI：10.1002/sim.8027
百度学术：Identification of cancer omics commonality and difference via community fusion
语言：外文
被引频次：3
人气指数：2
浏览次数：2
基金：National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11605288, 71771211]; MOE (Ministry of Education in China) Project of Humanities and Social Sciences [16YJCZH088]; Fund for building world-class universities (disciplines) of Renmin University of China; National Bureau of Statistics of China [2016LD01]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [CA204120, CA216017]
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