ArticleCancers2019
False Discovery Rate Control in Cancer Biomarker Selection Using Knockoffs.
Article in Cancers, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Statistical knockoffs improve biomarker discovery from transcriptomic data.Briefings in bioinformatics · 2026Article
- Spatially resolved epigenome sequencing via Tn5 transposition and deterministic DNA barcoding in tissue.Nature protocols · 2024Review
- Enhancing credit scoring accuracy with a comprehensive evaluation of alternative data.PloS one · 2024Article
- False discovery rate control in cancer biomarker selection.Genes & diseases · 2023Article
- Controlled variable selection in Weibull mixture cure models for high-dimensional data.Statistics in medicine · 2022Article
- Replicability in cancer omics data analysis: measures and empirical explorations.Briefings in bioinformatics · 2022Article
- Article
- Article
- Identifying Individual Medications Affecting Pulmonary Outcomes When Multiple Medications are Present.Clinical epidemiology · 2022Article
- Knockoff boosted tree for model-free variable selection.Bioinformatics (Oxford, England) · 2021Article
- High-dimensional variable selection for ordinal outcomes with error control.Briefings in bioinformatics · 2021Article
- Applications of Bioinformatics in Cancer.Cancers · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The discovery of biomarkers that are informative for cancer risk assessment, diagnosis, prognosis and treatment predictions is crucial. Recent advances in high-throughput genomics make it plausible to select biomarkers from the vast number of human genes in an unbiased manner. Yet, control of false discoveries is challenging given the large number of genes versus the relatively small number of patients in a typical cancer study. To ensure that most of the discoveries are true, we employ a knockoff procedure to control false discoveries. Our method is general and flexible, accommodating arbitrary covariate distributions, linear and nonlinear associations, and survival models. In simulations, our method compares favorably to the alternatives; its utility of identifying important genes in real clinical applications is demonstrated by the identification of seven genes associated with Breslow thickness in skin cutaneous melanoma patients.
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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.