Annals of Applied Statistics

Network-based feature screening with applications to genome data

Mengyun Wu, Liping Zhu, and Xingdong Feng

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Modern biological techniques have led to various types of data, which are often used to identify important biomarkers for certain diseases with appropriate statistical methods, such as feature screening. Model-free feature screening has been extensively studied in the literature, and it is effective to select useful predictors for ultra-high dimensional data. These existing screening procedures are conducted based on certain marginal correlations between predictors and a response variable, therefore network structures connecting the predictors are usually ignored. Google’s PageRank algorithm has achieved remarkable success. We adopt its spirit to adjust original screening approaches by incorporating the network information. We can then significantly improve the performance of those screening methods in choosing useful biomarkers, which is demonstrated in an intensive simulation study. A couple of real genome datasets along with a biological network are further analyzed by comparing results on both accuracy of predicting responses and stability of identifying biomarkers.

Article information

Ann. Appl. Stat., Volume 12, Number 2 (2018), 1250-1270.

Received: January 2017
Revised: May 2017
First available in Project Euclid: 28 July 2018

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Zentralblatt MATH identifier

Correlation feature screening model-free network ultra-high dimension variable selection


Wu, Mengyun; Zhu, Liping; Feng, Xingdong. Network-based feature screening with applications to genome data. Ann. Appl. Stat. 12 (2018), no. 2, 1250--1270. doi:10.1214/17-AOAS1097.

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Supplemental materials

  • Some additional tables. The Supplementary Materials includes some additional simulation results with different network structures, signal-noise-ratios and types of responses, the top 100 biomarkers for the dataset GSE71729 identified by DC-SIS-Network and the corresponding KEGG pathway analysis results.