ArticleBioMed research international2016
High Dimensional Variable Selection with Error Control.
Article in BioMed research international, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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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.
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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
6 citing papers in PubMed.
- Improved prognostic survival models for pediatric medulloblastoma using high dimensional gene expression data.BMC medical genomics · 2026Article
- Sparse-Input Neural Network using Group Concave Regularization.Transactions on machine learning research · 2025Article
- Variable Selection for Multivariate Failure Time Data via Regularized Sparse-Input Neural Network.Bioengineering (Basel, Switzerland) · 2025Article
- Hematological indicator-based machine learning models for preoperative prediction of lymph node metastasis in cervical cancer.Frontiers in oncology · 2024Article
- Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology.JCO precision oncology · 2019Article
- Combined Performance of Screening and Variable Selection Methods in Ultra-High Dimensional Data in Predicting Time-To-Event Outcomes.Diagnostic and prognostic research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Background. The iterative sure independence screening (ISIS) is a popular method in selecting important variables while maintaining most of the informative variables relevant to the outcome in high throughput data. However, it not only is computationally intensive but also may cause high false discovery rate (FDR). We propose to use the FDR as a screening method to reduce the high dimension to a lower dimension as well as controlling the FDR with three popular variable selection methods: LASSO, SCAD, and MCP. Method. The three methods with the proposed screenings were applied to prostate cancer data with presence of metastasis as the outcome. Results. Simulations showed that the three variable selection methods with the proposed screenings controlled the predefined FDR and produced high area under the receiver operating characteristic curve (AUROC) scores. In applying these methods to the prostate cancer example, LASSO and MCP selected 12 and 8 genes and produced AUROC scores of 0.746 and 0.764, respectively. Conclusions. We demonstrated that the variable selection methods with the sequential use of FDR and ISIS not only controlled the predefined FDR in the final models but also had relatively high AUROC scores.
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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.