ArticleBMC genetics2017
Network-based regularization for high dimensional SNP data in the case-control study of Type 2 diabetes.
Article in BMC genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Robust Cancer Biomarker Identification From Matched Transcriptomic Data Via Bootstrapped Regularized Conditional Logistic Regression.Cancer informatics · 2025Article
- A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.The clinical respiratory journal · 2023Article
- Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.Frontiers in genetics · 2023Article
- Identification of ferroptosis-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics.Frontiers in molecular biosciences · 2023Article
- Sparse group variable selection for gene-environment interactions in the longitudinal study.Genetic epidemiology · 2022Article
- Interep: An R Package for High-Dimensional Interaction Analysis of the Repeated Measurement Data.Genes · 2022Article
- A maximum flow-based network approach for identification of stable noncoding biomarkers associated with the multigenic neurological condition, autism.BioData mining · 2021Article
- Integrating Multi-Omics Data for Gene-Environment Interactions.Biotech (Basel (Switzerland)) · 2021Article
- Gene-Environment Interaction: A Variable Selection Perspective.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Assessment of Raman Spectroscopy for Reducing Unnecessary Biopsies for Melanoma Screening.Molecules (Basel, Switzerland) · 2020Article
- Incorporating genetic networks into case-control association studies with high-dimensional DNA methylation data.BMC bioinformatics · 2019Article
- Robust network-based regularization and variable selection for high-dimensional genomic data in cancer prognosis.Genetic epidemiology · 2019Article
- A Selective Review of Multi-Level Omics Data Integration Using Variable Selection.High-throughput · 2019Review
- Computational Modeling of Gene-Specific Transcriptional Repression, Activation and Chromatin Interactions in Leukemogenesis by LASSO-Regularized Logistic Regression.IEEE/ACM transactions on computational biology and bioinformaticsArticle
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7 authors.
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Abstract
backgroundOver the past decades, the prevalence of type 2 diabetes mellitus (T2D) has been steadily increasing around the world. Despite large efforts devoted to better understand the genetic basis of the disease, the identified susceptibility loci can only account for a small portion of the T2D heritability. Some of the existing approaches proposed for the high dimensional genetic data from the T2D case-control study are limited by analyzing a few number of SNPs at a time from a large pool of SNPs, by ignoring the correlations among SNPs and by adopting inefficient selection techniques.
methodsWe propose a network constrained regularization method to select important SNPs by taking the linkage disequilibrium into account. To accomodate the case control study, an iteratively reweighted least square algorithm has been developed within the coordinate descent framework where optimization of the regularized logistic loss function is performed with respect to one parameter at a time and iteratively cycle through all the parameters until convergence.
resultsIn this article, a novel approach is developed to identify important SNPs more effectively through incorporating the interconnections among them in the regularized selection. A coordinate descent based iteratively reweighed least squares (IRLS) algorithm has been proposed.
conclusionsBoth the simulation study and the analysis of the Nurses's Health Study, a case-control study of type 2 diabetes data with high dimensional SNP measurements, demonstrate the advantage of the network based approach over the competing alternatives.
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