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svm-based classifiers  (RNTech SAS)

 
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    RNTech SAS svm-based classifiers
    Svm Based Classifiers, supplied by RNTech SAS, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/svm-based+classifiers/svm+based+classifiers/pmc03022591-138-8-13
    Average 90 stars, based on 1 article reviews
    svm-based classifiers - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Serum Apolipoproteins C-I and C-III Are Reduced in Stomach Cancer Patients: Results from MALDI-Based Peptidome and Immuno-Based Clinical Assays
    Article Snippet: We received good results for each of the single-source classifiers; SVM-based classifiers for RNTech and Asterand had 90.0% and 93.0% predicted accuracies, respectively, according to ten-fold cross validation of the training set ( ).



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    Image Search Results


    Summary of studies applying radiomics for renal tumor differentiation.

    Journal: Cancers

    Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

    doi: 10.3390/cancers12061387

    Figure Lengend Snippet: Summary of studies applying radiomics for renal tumor differentiation.

    Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

    Techniques: Imaging, Comparison, Biomarker Discovery, Extraction, Diagnostic Assay, Mutagenesis, Selection

    Summary of studies applying radiomics to predict nuclear grade in ccRCC.

    Journal: Cancers

    Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

    doi: 10.3390/cancers12061387

    Figure Lengend Snippet: Summary of studies applying radiomics to predict nuclear grade in ccRCC.

    Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

    Techniques: Imaging, Biomarker Discovery, Selection, Filtration, Diffusion-based Assay, Variant Assay, Transformation Assay, Comparison

    Summary of studies applying radiomics and gene expression-based models to predict progression in ccRCC.

    Journal: Cancers

    Article Title: Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature

    doi: 10.3390/cancers12061387

    Figure Lengend Snippet: Summary of studies applying radiomics and gene expression-based models to predict progression in ccRCC.

    Article Snippet: Kocak et al. 2018 [ ] , , Three-phasic CECT , 68 RCC patients , , , , , 5.9 (3.3–8.1) , 5.9 (2.0–12.3) , ANN and ML-based SVM classifiers , 275 texture features extracted: , Distinguishing the three main RCC subtypes.

    Techniques: Expressing, Functional Assay, RNA Sequencing, Microarray, Gene Expression, Histopathology, Construct