ArticleBioinformatics (Oxford, England)2016
Component-wise gradient boosting and false discovery control in survival analysis with high-dimensional covariates.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Gradient boosting-based discrete failure time model for selecting time-varying effects and interactions.Lifetime data analysis · 2026Article
- Interpretable Machine Learning for Cross-Cohort Prediction of Motor Fluctuations in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Multi-cohort machine learning identifies predictors of cognitive impairment in Parkinson's disease.NPJ digital medicine · 2025Article
- Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network.BMC medical informatics and decision making · 2024Article
- A double-robust test for high-dimensional gene coexpression networks conditioning on clinical information.Biometrics · 2023Article
- Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer.Scientific reports · 2023Article
- Stratified Cox models with time-varying effects for national kidney transplant patients: A new blockwise steepest ascent method.Biometrics · 2022Article
- Where Do We Stand in Regularization for Life Science Studies?Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Cystic fibrosis-related diabetes onset can be predicted using biomarkers measured at birth.Genetics in medicine : official journal of the American College of Medical Genetics · 2021Article
- Clinical factors associated with rapid treatment of sepsis.PloS one · 2021Observational
- SurvBoost: An R Package for High-Dimensional Variable Selection in the Stratified Proportional Hazards Model via Gradient Boosting.The R journal · 2020Article
- Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients Using a Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study.JMIR medical informatics · 2020Article
- Covariance-Insured Screening.Computational statistics & data analysis · 2019Article
- Robust clinical marker identification for diabetic kidney disease with ensemble feature selection.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- An exploration of ontology-based EMR data abstraction for diabetic kidney disease prediction.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- False discovery control for penalized variable selections with high-dimensional covariates.Statistical applications in genetics and molecular biology · 2018Article
- Accommodating missingness in environmental measurements in gene-environment interaction analysis.Genetic epidemiology · 2017Article
- Ensembling Variable Selectors by Stability Selection for the Cox Model.Computational intelligence and neuroscience · 2017Article
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Authors and funding
11 authors.
Funding
Abstract
motivationTechnological advances that allow routine identification of high-dimensional risk factors have led to high demand for statistical techniques that enable full utilization of these rich sources of information for genetics studies. Variable selection for censored outcome data as well as control of false discoveries (i.e. inclusion of irrelevant variables) in the presence of high-dimensional predictors present serious challenges. This article develops a computationally feasible method based on boosting and stability selection. Specifically, we modified the component-wise gradient boosting to improve the computational feasibility and introduced random permutation in stability selection for controlling false discoveries.
resultsWe have proposed a high-dimensional variable selection method by incorporating stability selection to control false discovery. Comparisons between the proposed method and the commonly used univariate and Lasso approaches for variable selection reveal that the proposed method yields fewer false discoveries. The proposed method is applied to study the associations of 2339 common single-nucleotide polymorphisms (SNPs) with overall survival among cutaneous melanoma (CM) patients. The results have confirmed that BRCA2 pathway SNPs are likely to be associated with overall survival, as reported by previous literature. Moreover, we have identified several new Fanconi anemia (FA) pathway SNPs that are likely to modulate survival of CM patients. AVAILABILITY AND IMPLEMENTATION: The related source code and documents are freely available at https://sites.google.com/site/bestumich/issues. CONTACT: yili@umich.edu.
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