论文标题
基因组数据的成对非线性依赖性分析
Pairwise Nonlinear Dependence Analysis of Genomic Data
论文作者
论文摘要
在癌症基因组图集（TCGA）数据集中，基因对之间有许多有趣的非线性依赖性，这些基因揭示了重要的关系和癌症的亚型。这种基因组数据分析需要快速，强大和可解释的检测过程，尤其是在高维环境中。我们使用称为二元扩展测试的强大工具研究了TCGA基因对表达的非线性模式。我们发现许多非线性模式，其中一些是由已知的癌症亚型驱动的，其中一些是新颖的。
In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of pairs of genes from TCGA using a powerful tool called Binary Expansion Testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel.
