ArticleCancer informatics2016
Overlapping Group Logistic Regression with Applications to Genetic Pathway Selection.
Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- dioscRi enables transferable prediction of clinical outcomes in multi-parameter cytometry data.Nature communications · 2026Article
- Weighted overlapping group lasso for integrating prior network knowledge into gene set analysis.BMC bioinformatics · 2025Article
- A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.BMC medical research methodology · 2024Article
- A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.BMC bioinformatics · 2024Article
- Accounting for network noise in graph-guided Bayesian modeling of structured high-dimensional data.Biometrics · 2024Article
- Multicategory Survival Outcomes Classification via Overlapping Group Screening Process Based on Multinomial Logistic Regression Model With Application to TCGA Transcriptomic Data.Cancer informatics · 2024Article
- Development of a proteomic signature associated with severe disease for patients with COVID-19 using data from 5 multicenter, randomized, controlled, and prospective studies.Scientific reports · 2023Article
- Overlapping group screening for detection of gene-environment interactions with application to TCGA high-dimensional survival genomic data.BMC bioinformatics · 2022Article
- Computationally scalable regression modeling for ultrahigh-dimensional omics data with ParProx.Briefings in bioinformatics · 2021Article
- Adaptive group-regularized logistic elastic net regression.Biostatistics (Oxford, England) · 2021Article
- Multi-stage adaptive enrichment trial design with subgroup estimation.Journal of biopharmaceutical statistics · 2020Article
- Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information.BMC bioinformatics · 2019Article
- Article
- Overlapping group screening for detection of gene-gene interactions: application to gene expression profiles with survival trait.BMC bioinformatics · 2018Article
- Group spike-and-slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information.Bioinformatics (Oxford, England) · 2018Article
- Clinical Significance of Organic Anion Transporting Polypeptide Gene Expression in High-Grade Serous Ovarian Cancer.Frontiers in pharmacology · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Discovering important genes that account for the phenotype of interest has long been a challenge in genome-wide expression analysis. Analyses such as gene set enrichment analysis (GSEA) that incorporate pathway information have become widespread in hypothesis testing, but pathway-based approaches have been largely absent from regression methods due to the challenges of dealing with overlapping pathways and the resulting lack of available software. The R package grpreg is widely used to fit group lasso and other group-penalized regression models; in this study, we develop an extension, grpregOverlap, to allow for overlapping group structure using a latent variable approach. We compare this approach to the ordinary lasso and to GSEA using both simulated and real data. We find that incorporation of prior pathway information can substantially improve the accuracy of gene expression classifiers, and we shed light on several ways in which hypothesis-testing approaches such as GSEA differ from regression approaches with respect to the analysis of pathway data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.