水准点(测量)
钥匙(锁)
计算机科学
鉴定(生物学)
约束(计算机辅助设计)
计算生物学
基因组学
生物
基因
数学
基因组
遗传学
植物
几何学
计算机安全
地理
大地测量学
作者
Le Yang,Runpu Chen,Steve Goodison,Yijun Sun
标识
DOI:10.1038/s43588-020-00009-4
摘要
The identification of key functional biological networks from high-dimensional genomics data is pivotal for cancer research. Here, we introduce FDRnet, a method for the detection of molecular subnetworks in cancer, which addresses several challenges in pathway analysis. FDRnet detects key subnetworks by solving a mixed-integer linear programming problem, using a given upper bound of false discovery rate (FDR) as a budget constraint, and minimizing a conductance score to find dense subgraphs around seed genes. A large-scale benchmark study was performed on both simulation and cancer genomics data. FDRnet outperformed other methods in the ability to detect functionally homogeneous subnetworks in a scale-free biological network, to control FDRs of the genes in detected subnetworks, to improve computational efficiency and to integrate multi-omics data. By overcoming the limitations of existing approaches, FDRnet can facilitate the detection of key functional pathways in cancer and other genetic diseases.
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