ArticleMolecules (Basel, Switzerland)2017
Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics.
Article in Molecules (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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
76 citing papers in PubMed.
- Bioinformatics analysis reveals shared gene signatures and molecular mechanisms between periodontitis and rheumatoid arthritis in the context of aging.Acta odontologica Scandinavica · 2026Article
- Machine learning-based identification of lactate metabolism-associated biomarkers in non-alcoholic fatty liver disease.Clinical and experimental medicine · 2026Article
- Anoctamin 5 as a protective factor in prostate cancer: Insights from WGCNA, machine learning, and experimental analysis, with a focus on the anoctamin family.Translational oncology · 2026Article
- Identification of GCNT3 as a glycometabolism-associated biomarker in endometrial cancer.Scientific reports · 2026Article
- Decoding the significance of TERT gene in Polycystic Ovary Syndrome: From molecular mechanisms to emerging diagnostic and therapeutic strategies.Journal of ovarian research · 2026Review
- A transcriptome-based tool for assessing skin aging reveals the ameliorative effect of stem cell-derived extracellular vesicles on UVB-induced aging.BMC genomics · 2026Article
- Identification of Chondrocyte Stemness-Associated Biomarkers in Osteoarthritis Using Bioinformatics and Machine Learning Approaches.Journal of inflammation research · 2026Article
- Deciphering MMRN1 diagnostic and therapeutic implications in the substantia nigra of Parkinson's disease patients via integrative bioinformatic analysis and multi-omics studies.Frontiers in aging neuroscience · 2026Article
- A machine learning-derived genomic dataset from bacteria frequently reported as probiotics.Frontiers in bioinformatics · 2026Article
- Identification of lactylation and its hub genes in contributing immune activation and renal allograft fibrosis by integrative bioinformatics and machine learning.Frontiers in immunology · 2026Article
- Identification of candidate selenium-associated transcriptomic biomarkers in chronic obstructive pulmonary disease: an integrated bioinformatics and expression validation study.Frontiers in medicine · 2026Article
- Identifying potential biomarkers for type 2 diabetes in the adipose tissue of older adults via multiple machine learning algorithms.Scientific reports · 2025Article
- FREM1 serves as a novel therapeutic target in breast cancer through basement membrane-based prognostic modeling with integrated bioinformatics and experimental validation.Discover oncology · 2025Article
- Liquid-liquid phase separation-related genes in the immune microenvironment and diagnosis of focal segmental glomerulosclerosis and minimal change disease.Renal failure · 2025Article
- Unveiling the role of oxidative stress in ANCA-associated glomerulonephritis through integrated machine learning and bioinformatics analyses.Renal failure · 2025Article
- SHAP-based binarization enhances metataxonomic machine learning with application to gut microbiota of inflammatory bowel disease.Scientific reports · 2025Article
- Integrated machine learning identifies biomarkers for bilirubin-induced Alzheimer's disease-like lesions in neonates and adults.Scientific reports · 2025Article
- Development and validation of a predictive model for diabetic peripheral neuropathy with type 2 diabetes mellitus in Xinjiang, China.Scientific reports · 2025Article
- Neutrophil extracellular trapping network-associated biomarkers in liver fibrosis: machine learning and experimental validation.Journal of translational medicine · 2025Article
- Identification and validation of epithelial‑mesenchymal transition‑related genes for diabetic nephropathy by WGCNA and machine learning.Molecular medicine reports · 2025Article
16 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Feature selection is an important topic in bioinformatics. Defining informative features from complex high dimensional biological data is critical in disease study, drug development, etc. Support vector machine-recursive feature elimination (SVM-RFE) is an efficient feature selection technique that has shown its power in many applications. It ranks the features according to the recursive feature deletion sequence based on SVM. In this study, we propose a method, SVM-RFE-OA, which combines the classification accuracy rate and the average overlapping ratio of the samples to determine the number of features to be selected from the feature rank of SVM-RFE. Meanwhile, to measure the feature weights more accurately, we propose a modified SVM-RFE-OA (M-SVM-RFE-OA) algorithm that temporally screens out the samples lying in a heavy overlapping area in each iteration. The experiments on the eight public biological datasets show that the discriminative ability of the feature subset could be measured more accurately by combining the classification accuracy rate with the average overlapping degree of the samples compared with using the classification accuracy rate alone, and shielding the samples in the overlapping area made the calculation of the feature weights more stable and accurate. The methods proposed in this study can also be used with other RFE techniques to define potential biomarkers from big biological data.
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.