ReviewBriefings in bioinformatics2015
A selective review of robust variable selection with applications in bioinformatics.
Review in Briefings in bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
63 citing papers in PubMed.
- Polygenic Score Identifies Athletes at Increased Risk for Slower Recovery After Sport-Related Concussion: A Concussion Assessment, Research, and Education (CARE) Consortium Study.Sports medicine (Auckland, N.Z.) · 2026Article
- Analysis of Health-Related Quality of Life in Multiple Sclerosis: A Bayesian Quantile LASSO Approach.Healthcare (Basel, Switzerland) · 2026Article
- Robust prioritization of genomic features with stability selection.Bioinformatics (Oxford, England) · 2026Article
- Multi-omics data integration using time-to event endpoint and supervised Cox penalized regression: a comprehensive review.Clinical and experimental medicine · 2026Review
- Robust sparse Bayesian regression for longitudinal gene-environment interactions.Journal of the Royal Statistical Society. Series C, Applied statistics · 2025Article
- Efficient blockLASSO for polygenic scores with applications to all of us and UK Biobank.BMC genomics · 2025Article
- Article
- The spike-and-slab quantile LASSO for robust variable selection in cancer genomics studies.Statistics in medicine · 2024Article
- Robust variable selection methods with Cox model-a selective practical benchmark study.Briefings in bioinformatics · 2024Review
- Is Seeing Believing? A Practitioner's Perspective on High-Dimensional Statistical Inference in Cancer Genomics Studies.Entropy (Basel, Switzerland) · 2024Review
- Computational dissection of genetic variation modulating the response of multiple photosynthetic phenotypes to the light environment.BMC genomics · 2024Article
- Review
- The Bayesian Regularized Quantile Varying Coefficient Model.Computational statistics & data analysis · 2023Article
- Identification and validation of a DNA methylation-driven gene-based prognostic model for clear cell renal cell carcinoma.BMC genomics · 2023Article
- Article
- Identification of an individualized therapy prognostic signature for head and neck squamous cell carcinoma.BMC genomics · 2023Article
- Article
- Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.Academic radiology · 2023Review
- Springer: An R package for bi-level variable selection of high-dimensional longitudinal data.Frontiers in genetics · 2023Article
- The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review.Cancers · 2022Review
3 more citing papers are in PubMed but not listed here.
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
A drastic amount of data have been and are being generated in bioinformatics studies. In the analysis of such data, the standard modeling approaches can be challenged by the heavy-tailed errors and outliers in response variables, the contamination in predictors (which may be caused by, for instance, technical problems in microarray gene expression studies), model mis-specification and others. Robust methods are needed to tackle these challenges. When there are a large number of predictors, variable selection can be as important as estimation. As a generic variable selection and regularization tool, penalization has been extensively adopted. In this article, we provide a selective review of robust penalized variable selection approaches especially designed for high-dimensional data from bioinformatics and biomedical studies. We discuss the robust loss functions, penalty functions and computational algorithms. The theoretical properties and implementation are also briefly examined. Application examples of the robust penalization approaches in representative bioinformatics and biomedical studies are also illustrated.
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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.