ReviewCancer genomics & proteomics
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.
Review in Cancer genomics & proteomics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 669 papers, 4 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
669 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Support Vector Machine Models for Cancer Detection and Related Clinical Applications Using Omics and Omics-Adjacent Data: A Systematic Review.BioMed research international · 2026Pooled it
- Artificial intelligence for genomic science: a scoping review of concepts, architectures, applications, and open challenges.Frontiers in bioinformatics · 2026Pooled it
- Diagnostic performance of radiomics models for preoperative prediction of microsatellite instability status in endometrial cancer: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- The Use of Machine Learning for Analyzing Real-World Data in Disease Prediction and Management: Systematic Review.JMIR medical informatics · 2025Pooled it
- Multiple machine learning models for predicting major adverse cardiovascular events in dialysis with clinical and echocardiographic parameters: a retrospective cohort study.Annals of medicine · 2026Article
- Tangermeme: a toolkit for understanding cis-regulatory logic using deep learning models.Nature methods · 2026Article
- A Computed Tomography Radiomics Approach for Assessing the T Cell-Inflamed Gene Expression Profile in Hepatocellular Carcinoma and Its Association With Immunotherapy Response.Health care science · 2026Article
- Multi-omics and machine learning identify ALOX5 as a ferroptosis-associated driver of epithelial barrier dysfunction in chronic rhinosinusitis with nasal polyps.The World Allergy Organization journal · 2026Article
- Brain stroke detection and classification using deep learning-based transCBAMSegNet and hybrid transformer models.Physical and engineering sciences in medicine · 2026Article
- Classification of intracardiac heart sounds for ventricular and atrial fibrillation based on feature fusion and voting ensemble learning.Medical & biological engineering & computing · 2026Article
- Comprehensive Analysis of Cuproptosis-Related Genes According to Cancer Stage and Their Prognostic Value in Cervical Cancer.International journal of molecular sciences · 2026Article
- LungCNET: A High-Performance Deep CNN Model for Lung Cancer Detection Evaluated Against Widely Used CNN Benchmarks.Bioengineering (Basel, Switzerland) · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Machine learning-driven cancer diagnostics with improved robustness and interpretability.Chemical science · 2026Review
- An explainable machine learning model for predicting high phosphorus risk in patients on maintenance hemodialysis: a multicenter retrospective study.BMC medical informatics and decision making · 2026Article
- Microscopy Cell Segmentation: Review and Benchmarking of Task-Specific and Foundation Models.Journal of imaging · 2026Review
- Radiomic Signature of the Substantia Nigra on Neuromelanin-Sensitive MRI Distinguishes Bipolar II Disorder From Unipolar Depression.Brain and behavior · 2026Article
- Identification and validation of BLK and OSBPL10 as diagnostic and prognostic biomarkers for nasopharyngeal carcinoma through machine learning algorithms.Scientific reports · 2026Article
- Dissecting T-cell exhaustion heterogeneity and immune ecosystem dynamics in colorectal cancer through multi-omics machine learning.BMC cancer · 2026Article
- Screening of Natural Product-Derived USP7 Inhibitors for Cancer Therapy via Integrated Machine Learning and Molecular Simulations.Current issues in molecular biology · 2026Article
609 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
Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications.
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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.