ReviewCancer informatics2014
Overcome support vector machine diagnosis overfitting.
Review in Cancer informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 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.
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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
45 citing papers in PubMed.
- Article
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- Spectral Region Optimization and Machine Learning-Based Nonlinear Spectral Analysis for Raman Detection of Cardiac Fibrosis Following Myocardial Infarction.International journal of molecular sciences · 2025Article
- MRI-based radiomics model for differentiating focal cortical dysplasia from dysembryoplastic neuroepithelial tumor in epileptic children.Frontiers in neurology · 2025Article
- Article
- Colocalization of Cancer-Associated Biomarkers on Single Extracellular Vesicles for Early Detection of Cancer.The Journal of molecular diagnostics : JMD · 2024Article
- Intracranial EEG signals disentangle multi-areal neural dynamics of vicarious pain perception.Nature communications · 2024Article
- Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases.Scientific reports · 2024Article
- Super-Enhancers and Their Parts: From Prediction Efforts to Pathognomonic Status.International journal of molecular sciences · 2024Review
- Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning.Frontiers in neuroinformatics · 2024Article
- Morphological Species Delimitation in The Western Pond Turtle (Integrative organismal biology (Oxford, England) · 2024Article
- Foundations for improved vaccine correlate of risk analysis using positive-unlabeled learning.Human vaccines & immunotherapeutics · 2023Article
- Differentiation between cerebral alveolar echinococcosis and brain metastases with radiomics combined machine learning approach.European journal of medical research · 2023Article
- A Quantitative Assessment Grading Study of Balance Performance Based on Lower Limb Dataset.Sensors (Basel, Switzerland) · 2022Article
- Smartphone Camera Identification from Low-Mid Frequency DCT Coefficients of Dark Images.Entropy (Basel, Switzerland) · 2022Article
- Current challenges and best practices for cell-free long RNA biomarker discovery.Biomarker research · 2022Review
- Artificial intelligence and spine imaging: limitations, regulatory issues and future direction.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2022Review
- Article
- Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Support vector machines (SVMs) are widely employed in molecular diagnosis of disease for their efficiency and robustness. However, there is no previous research to analyze their overfitting in high-dimensional omics data based disease diagnosis, which is essential to avoid deceptive diagnostic results and enhance clinical decision making. In this work, we comprehensively investigate this problem from both theoretical and practical standpoints to unveil the special characteristics of SVM overfitting. We found that disease diagnosis under an SVM classifier would inevitably encounter overfitting under a Gaussian kernel because of the large data variations generated from high-throughput profiling technologies. Furthermore, we propose a novel sparse-coding kernel approach to overcome SVM overfitting in disease diagnosis. Unlike traditional ad-hoc parametric tuning approaches, it not only robustly conquers the overfitting problem, but also achieves good diagnostic accuracy. To our knowledge, it is the first rigorous method proposed to overcome SVM overfitting. Finally, we propose a novel biomarker discovery algorithm: Gene-Switch-Marker (GSM) to capture meaningful biomarkers by taking advantage of SVM overfitting on single genes.
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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.