Analysis of cancer gene expression data with an assisted robust marker identification approach
In this study, we develop an ARMI (assisted robust marker identification) approach for analyzing cancer studies with measurements on GEs as well as regulators. The proposed approach borrows information from regulators and can be more effective than analyzing GE data alone. A robust objective function is adopted to accommodate long‐tailed distributions. Marker identification is effectively realized using penalization. The proposed approach has an intuitive formulation and is computationally much affordable. Simulation shows its satisfactory performance under a variety of settings. TCGA (The Cancer Genome Atlas) data on melanoma and lung cancer are analyzed, which leads to biologically plausible marker identification and superior prediction.
Source: Genetic Epidemiology - Category: Epidemiology Authors: Hao Chai, Xingjie Shi, Qingzhao Zhang, Qing Zhao, Yuan Huang, Shuangge Ma Tags: RESEARCH ARTICLE Source Type: research
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