ArticleGenetic epidemiology2019
Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis.
Article in Genetic epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Analysis of Health-Related Quality of Life in Multiple Sclerosis: A Bayesian Quantile LASSO Approach.Healthcare (Basel, Switzerland) · 2026Article
- Differentially Expressed Genes Identify FIGO Stage II Cervical Cancer Patients with a Higher Risk of Relapse in a Small Cohort.Journal of personalized medicine · 2025Article
- A Comprehensive Review of Deep Learning Applications with Multi-Omics Data in Cancer Research.Genes · 2025Review
- Efficient blockLASSO for polygenic scores with applications to all of us and UK Biobank.BMC genomics · 2025Article
- MMOSurv: meta-learning for few-shot survival analysis with multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.Statistics in medicine · 2024Article
- Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.Briefings in bioinformatics · 2024Article
- Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package.Scientific reports · 2023Article
- Identification and validation of a DNA methylation-driven gene-based prognostic model for clear cell renal cell carcinoma.BMC genomics · 2023Article
- Article
- Identification of an individualized therapy prognostic signature for head and neck squamous cell carcinoma.BMC genomics · 2023Article
- Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.Frontiers in genetics · 2023Article
- Article
- Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.Journal of computational biology : a journal of computational molecular cell biology · 2022Review
- Feature selection and classification over the network with missing node observations.Statistics in medicine · 2022Article
- Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data.Genes · 2022Article
- Ferroptosis-associated DNA methylation signature predicts overall survival in patients with head and neck squamous cell carcinoma.BMC genomics · 2022Article
- Identification of aberrantly methylated differentially expressed genes and pro-tumorigenic role of KIF2C in melanoma.Frontiers in genetics · 2022Article
- Prognostic Value ofFrontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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, because of 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 model. Extensive simulation studies show the advantage of the proposed method over the alternative methods. Two case studies of lung cancer datasets with high-dimensional gene expression measurements demonstrate that the proposed approach has identified markers with important implications.
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