ArticlePloS one2011
The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
107 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review.Journal of cancer survivorship : research and practice · 2025Pooled it
- Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis.Nature communications · 2025Trial
- Prognosis Relevance of Serum Cytokines in Pancreatic Cancer.BioMed research international · 2015Trial
- Review
- Measuring the optimistic bias of cross-validation in radiomics.Scientific reports · 2026Article
- Improved prognostic survival models for pediatric medulloblastoma using high dimensional gene expression data.BMC medical genomics · 2026Article
- Transcriptomic Evidence of Immune-Tumor Uncoupling Defines a High-Risk State in Uterine Corpus Endometrial Carcinoma.International journal of molecular sciences · 2026Article
- Statistical knockoffs improve biomarker discovery from transcriptomic data.Briefings in bioinformatics · 2026Article
- Development and evaluation of multi-strategy fusion machine learning models for predicting postoperative recurrence in non-small cell lung cancer.BMC medical imaging · 2026Article
- A Stability-Oriented Biomarker Selection Framework Synergistically Driven by Robust Rank Aggregation and L1-Sparse Modeling.Metabolites · 2025Article
- Assessing Obstructive Sleep Apnea Severity During Wakefulness via Tracheal Breathing Sound Analysis.Sensors (Basel, Switzerland) · 2025Article
- Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.Biotech (Basel (Switzerland)) · 2025Review
- Sparse multitask group Lasso for genome-wide association studies.PLoS computational biology · 2025Article
- Early detection of autism spectrum disorder: gait deviations and machine learning.Scientific reports · 2025Article
- CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.The British journal of radiology · 2024Article
- Performance and clinical utility of a new supervised machine-learning pipeline in detecting rare ciliopathy patients based on deep phenotyping from electronic health records and semantic similarity.Orphanet journal of rare diseases · 2024Article
- Machine-learning methods based on the texture and non-texture features of MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer.Translational cancer research · 2023Article
- A Fusion-Based Machine Learning Approach for Autism Detection in Young Children Using Magnetoencephalography Signals.Journal of autism and developmental disorders · 2023Article
- Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer.Scientific reports · 2023Article
47 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. In this study we compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Surprisingly, complex wrapper and embedded methods generally do not outperform simple univariate feature selection methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results.
Indexed as
Identifiers
What OpenQuestion holds
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.