machine svm mean accuracy Search Results


selection operator lr logistic regression svm support vector machines tcga  (Genovis Inc)
99
Genovis Inc selection operator lr logistic regression svm support vector machines tcga
Selection Operator Lr Logistic Regression Svm Support Vector Machines Tcga, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/selection operator lr logistic regression svm support vector machines tcga/product/Genovis Inc
Average 99 stars, based on 1 article reviews
selection operator lr logistic regression svm support vector machines tcga - by Bioz Stars, 2026-03
99/100 stars
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svm classifier  (MathWorks Inc)
90
MathWorks Inc svm classifier
Svm Classifier, supplied by MathWorks Inc, 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/result/svm classifier/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm classifier - by Bioz Stars, 2026-03
90/100 stars
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linear svm classifier  (MathWorks Inc)
90
MathWorks Inc linear svm classifier
Linear Svm Classifier, supplied by MathWorks Inc, 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/result/linear svm classifier/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
linear svm classifier - by Bioz Stars, 2026-03
90/100 stars
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support vector machine (svm) classification routine  (MathWorks Inc)
90
MathWorks Inc support vector machine (svm) classification routine
Support Vector Machine (Svm) Classification Routine, supplied by MathWorks Inc, 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/result/support vector machine (svm) classification routine/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
support vector machine (svm) classification routine - by Bioz Stars, 2026-03
90/100 stars
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support vector machine svm classifier  (MathWorks Inc)
96
MathWorks Inc support vector machine svm classifier
Support Vector Machine Svm Classifier, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/support vector machine svm classifier/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
support vector machine svm classifier - by Bioz Stars, 2026-03
96/100 stars
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svm-based sar models  (Tanabe)
90
Tanabe svm-based sar models
Svm Based Sar Models, supplied by Tanabe, 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/result/svm-based sar models/product/Tanabe
Average 90 stars, based on 1 article reviews
svm-based sar models - by Bioz Stars, 2026-03
90/100 stars
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svm classifications with a radial basis function kernel (rbf-svm)  (MathWorks Inc)
90
MathWorks Inc svm classifications with a radial basis function kernel (rbf-svm)
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
Svm Classifications With A Radial Basis Function Kernel (Rbf Svm), supplied by MathWorks Inc, 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/result/svm classifications with a radial basis function kernel (rbf-svm)/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
svm classifications with a radial basis function kernel (rbf-svm) - by Bioz Stars, 2026-03
90/100 stars
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linear support vector machine (svm) classifiers  (MathWorks Inc)
90
MathWorks Inc linear support vector machine (svm) classifiers
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
Linear Support Vector Machine (Svm) Classifiers, supplied by MathWorks Inc, 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/result/linear support vector machine (svm) classifiers/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
linear support vector machine (svm) classifiers - by Bioz Stars, 2026-03
90/100 stars
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k-nearest neighbors (knn)  (MathWorks Inc)
90
MathWorks Inc k-nearest neighbors (knn)
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
K Nearest Neighbors (Knn), supplied by MathWorks Inc, 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/result/k-nearest neighbors (knn)/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
k-nearest neighbors (knn) - by Bioz Stars, 2026-03
90/100 stars
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machine leaning toolbox  (MathWorks Inc)
96
MathWorks Inc machine leaning toolbox
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
Machine Leaning Toolbox, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/machine leaning toolbox/product/MathWorks Inc
Average 96 stars, based on 1 article reviews
machine leaning toolbox - by Bioz Stars, 2026-03
96/100 stars
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k-nearest neighbor (knn)  (Partek)
90
Partek k-nearest neighbor (knn)
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
K Nearest Neighbor (Knn), supplied by Partek, 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/result/k-nearest neighbor (knn)/product/Partek
Average 90 stars, based on 1 article reviews
k-nearest neighbor (knn) - by Bioz Stars, 2026-03
90/100 stars
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svm  (Okabe Co Ltd)
90
Okabe Co Ltd svm
Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of <t>multiparametric</t> <t>MRI</t> values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding <t>RBF-SVM</t> models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01
Svm, supplied by Okabe Co Ltd, 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/result/svm/product/Okabe Co Ltd
Average 90 stars, based on 1 article reviews
svm - by Bioz Stars, 2026-03
90/100 stars
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Image Search Results


Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of multiparametric MRI values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01

Journal: Journal of Biomedical Science

Article Title: Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

doi: 10.1186/s12929-021-00750-6

Figure Lengend Snippet: Results of support vector machine analysis to discriminate cancer from normal tissue in the prostate transition zone. Left: ROC plot of combinations of multiparametric MRI values. T2WI + DWI is in blue and T2WI + DWI + DCE in green. Without MRSI is represented by a dashed-line and with MRSI by a solid-line. Only multiparametric MRI parameters with statistically significant differences between cancer and normal tissues ( p < 0.05) were used for classification. Right: Bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01

Article Snippet: We developed a machine learning platform for mp-MRI including support vector machine (SVM) classifications with a radial basis function kernel (RBF-SVM) and area under receiver operator characteristic (ROC) analyses using an in-house Matlab routine to evaluate the diagnostic performance of models with different parametric combinations: T2WI + DWI, T2WI + DWI + DCE, T2WI + DWI + MRSI, and T2WI + DWI + DCE + MRSI.

Techniques: Plasmid Preparation, Comparison

Results of support vector machine analysis to separate tumor aggressiveness classes. Left side: ROC curves of the six RBF-SVM models for low-risk vs high-risk cancer, low-risk vs intermediate-risk cancer and intermediate-risk vs high-risk cancer. A leave-one-out cross-validation technique was used for the combined ADC and K trans (dashed line) and all the combined ADC, K trans and metabolite ratios (solid line) with a significant difference between the two groups ( p < 0.05). Right side: bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01 and * p < 0.05

Journal: Journal of Biomedical Science

Article Title: Diagnosis of transition zone prostate cancer by multiparametric MRI: added value of MR spectroscopic imaging with sLASER volume selection

doi: 10.1186/s12929-021-00750-6

Figure Lengend Snippet: Results of support vector machine analysis to separate tumor aggressiveness classes. Left side: ROC curves of the six RBF-SVM models for low-risk vs high-risk cancer, low-risk vs intermediate-risk cancer and intermediate-risk vs high-risk cancer. A leave-one-out cross-validation technique was used for the combined ADC and K trans (dashed line) and all the combined ADC, K trans and metabolite ratios (solid line) with a significant difference between the two groups ( p < 0.05). Right side: bar charts of AUC values, sensitivity and specificity of the corresponding RBF-SVM models. McNemar test was used for pairwise comparison of sensitivity and specificities of models and Delong test was calculated for pairwise comparison of AUC of models. ** p < 0.01 and * p < 0.05

Article Snippet: We developed a machine learning platform for mp-MRI including support vector machine (SVM) classifications with a radial basis function kernel (RBF-SVM) and area under receiver operator characteristic (ROC) analyses using an in-house Matlab routine to evaluate the diagnostic performance of models with different parametric combinations: T2WI + DWI, T2WI + DWI + DCE, T2WI + DWI + MRSI, and T2WI + DWI + DCE + MRSI.

Techniques: Plasmid Preparation, Biomarker Discovery, Comparison