ArticleBMC genomics2016
Stable feature selection based on the ensemble L
Article in BMC genomics, 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.
- Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.Brain research bulletin · 2026Article
- An LSEC-focused computational drug repurposing platform for liver fibrosis: Identification of vorinostat and other LSEC-protective candidates.bioRxiv : the preprint server for biology · 2026Article
- An Artificial Intelligence-Based Model to Predict Pregnancy After Intrauterine Insemination: A Retrospective Analysis of 9501 Cycles.Journal of personalized medicine · 2025Article
- Correspondence on "Early dynamics of clinical and laboratory parameters predict primary refractory disease in patients with metastatic urothelial carcinoma receiving atezolizumab"by GraserJournal for immunotherapy of cancer · 2025Article
- Response to: Correspondence on "Early dynamics of clinical and laboratory parameters predict primary refractory disease in patients with metastatic urothelial carcinoma receiving atezolizumab" by GaoJournal for immunotherapy of cancer · 2025Article
- CCNB1 is a novel prognostic biomarker and promotes proliferation, migration and invasion in Wilms tumor.BMC medical genomics · 2023Article
- A Method for Increasing the Robustness of Stable Feature Selection for Biomarker Discovery in Molecular Medicine Developed Using Serum Small Extracellular Vesicle Associated miRNAs and the Barrett's Oesophagus Disease Spectrum.International journal of molecular sciences · 2023Article
- A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.Diagnostics (Basel, Switzerland) · 2022Article
- Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology.Scientific reports · 2021Article
- RNA sequencing: new technologies and applications in cancer research.Journal of hematology & oncology · 2020Review
- Machine learning predicts live-birth occurrence before in-vitro fertilization treatment.Scientific reports · 2020Article
- Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data.BMC genomics · 2020Article
- Perspective: Guiding Principles for the Implementation of Personalized Nutrition Approaches That Benefit Health and Function.Advances in nutrition (Bethesda, Md.) · 2020Article
- Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time.BioMed research international · 2019Article
- Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury.Scientific reports · 2018Article
- Deep Learning Methods for Predicting Disease Status Using Genomic Data.Journal of biometrics & biostatistics · 2018Article
- An Ensemble Feature Selection Method for Biomarker Discovery.Proceedings of the ... IEEE International Symposium on Signal Processing and Information Technology. IEEE International Symposium on Signal Processing and Information Technology · 2017Article
- 2016 update on APBioNet's annual international conference on bioinformatics (InCoB).BMC genomics · 2016Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLately, biomarker discovery has become one of the most significant research issues in the biomedical field. Owing to the presence of high-throughput technologies, genomic data, such as microarray data and RNA-seq, have become widely available. Many kinds of feature selection techniques have been applied to retrieve significant biomarkers from these kinds of data. However, they tend to be noisy with high-dimensional features and consist of a small number of samples; thus, conventional feature selection approaches might be problematic in terms of reproducibility.
resultsIn this article, we propose a stable feature selection method for high-dimensional datasets. We apply an ensemble L
conclusionA comparison with established algorithms, i.e., a fast correlation-based filter, random forest, and an ensemble version of an L
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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.