论文标题
通过社区融合来识别癌症的普遍性和差异
Identification of cancer omics commonality and difference via community fusion
论文作者
论文摘要
癌症法学数据的分析是一个“经典”问题，但仍然具有挑战性。一些最新研究的早期研究的前进，一些最近的研究分析了多种“相关”癌症类型/亚型的数据，研究了它们的共同点和差异，并带来了深刻的发现。在本文中，我们考虑了多个OMIC数据集的分析，每个数据集都在“相关”癌症的一种类型/亚型上。开发了一种社区融合方法（COFU）方法，该方法使用新颖的惩罚技术进行标记选择和模型构建，并提供信息可容纳OMICS测量的网络社区结构，并自动识别癌症OMICS标记的共同点和差异。模拟证明了其优于直接竞争对手。 TCGA肺癌和黑色素瘤数据的分析导致有趣的发现
The analysis of cancer omics data is a "classic" problem, however, 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
