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Activity Number: 237 - Feature Selection and Statistical Learning in Genomics
Type: Contributed
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #324765
Title: Robust Network-Based Regularization and Variable Selection for High-Dimensional Genomic Data in Cancer Prognosis
Author(s): Jie Ren* and Yinhao Du and Dewey Molenda and Yu Jiang and Wu Cen
Companies: Department of Statistics, Kansas State University and Department of Statistics, Kansas State University and Department of Statistics, Kansas State University and School of Public Health, University of Memphis and Department of Statistics, Kansas State University
Keywords: Network-based regularization ; Robustness ; Cancer prognosis ; Variable selection

In cancer genomic studies, an important objective is to identify prognostic markers associated with patients' survival. Network-based regularization has achieved success in variable selections for high-dimensional cancer genomic data, due to its ability to incorporate the correlations among genomic features. However, as survival time data usually follow skewed distributions, and are contaminated by outliers, network-constrained regularization that does not take the robustness into account leads to false identifications of network structure and biased estimation of patients' survival. In this study, we develop a novel robust network based variable selection method under the accelerated failure time (AFT) model. Extensive simulation studies show the advantage of the proposed method over the alternatives in terms of identification and predictive performance. A case study of The Cancer Genome Atlas (TCGA) lung cancer data with high dimensional gene expression measurements demonstrates that the proposed approach has identified markers with important implications.

Authors who are presenting talks have a * after their name.

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