svm models Search Results


svm model  (Murty Pharmaceuticals)
90
Murty Pharmaceuticals svm model
Svm Model, supplied by Murty Pharmaceuticals, 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 model/product/Murty Pharmaceuticals
Average 90 stars, based on 1 article reviews
svm model - by Bioz Stars, 2026-03
90/100 stars
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svm-clinic-labtest model  (LABTEST DIAGNOSTICA)
90
LABTEST DIAGNOSTICA svm-clinic-labtest model
Svm Clinic Labtest Model, supplied by LABTEST DIAGNOSTICA, 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-clinic-labtest model/product/LABTEST DIAGNOSTICA
Average 90 stars, based on 1 article reviews
svm-clinic-labtest model - by Bioz Stars, 2026-03
90/100 stars
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svm model  (COMSOL Inc)
90
COMSOL Inc svm model
Svm Model, supplied by COMSOL 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 model/product/COMSOL Inc
Average 90 stars, based on 1 article reviews
svm model - by Bioz Stars, 2026-03
90/100 stars
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svm models  (Geron Bio)
90
Geron Bio svm models
Svm Models, supplied by Geron Bio, 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 models/product/Geron Bio
Average 90 stars, based on 1 article reviews
svm models - by Bioz Stars, 2026-03
90/100 stars
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optimization c svm model using the rbf kernel  (Geron Bio)
90
Geron Bio optimization c svm model using the rbf kernel
Optimization C Svm Model Using The Rbf Kernel, supplied by Geron Bio, 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/optimization c svm model using the rbf kernel/product/Geron Bio
Average 90 stars, based on 1 article reviews
optimization c svm model using the rbf kernel - by Bioz Stars, 2026-03
90/100 stars
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svm model  (Theragnostic Technologies)
90
Theragnostic Technologies svm model
Svm Model, supplied by Theragnostic Technologies, 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 model/product/Theragnostic Technologies
Average 90 stars, based on 1 article reviews
svm model - by Bioz Stars, 2026-03
90/100 stars
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ls-svm model  (Wolters Kluwer Health)
90
Wolters Kluwer Health ls-svm model
Ls Svm Model, supplied by Wolters Kluwer Health, 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/ls-svm model/product/Wolters Kluwer Health
Average 90 stars, based on 1 article reviews
ls-svm model - by Bioz Stars, 2026-03
90/100 stars
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svm classification model  (Cognivue Inc)
90
Cognivue Inc svm classification model
Svm Classification Model, supplied by Cognivue 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 classification model/product/Cognivue Inc
Average 90 stars, based on 1 article reviews
svm classification model - by Bioz Stars, 2026-03
90/100 stars
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svm modeling  (Tanabe)
90
Tanabe svm modeling
Svm Modeling, 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 modeling/product/Tanabe
Average 90 stars, based on 1 article reviews
svm modeling - by Bioz Stars, 2026-03
90/100 stars
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svm model  (Johns Hopkins HealthCare)
90
Johns Hopkins HealthCare svm model
a.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the originally published LDA model in women without a psychiatric history. b.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the <t>SVM</t> model in women without a psychiatric history. c.) Plot of EPDS threshold values (x axis) as a function of the mean model output (predicted probability) for women above the EPDS threshold minus that for women below the threshold (y axis) for the originally published LDA model in women without a psychiatric history. d.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model in women with a previous history <t>of</t> <t>PPD.</t> e.) Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women with a previous history of PPD. f.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women without a psychiatric history. g.) Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. h.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. Horizontal dashed red lines denote an AUC of 70% while a dashed vertical black line denotes an EPDS of ≥ 13, signifying likely PPD.
Svm Model, supplied by Johns Hopkins HealthCare, 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 model/product/Johns Hopkins HealthCare
Average 90 stars, based on 1 article reviews
svm model - by Bioz Stars, 2026-03
90/100 stars
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baseline model svm  (Chennai Corporation)
90
Chennai Corporation baseline model svm
Comparison table of baseline models vs. our study using R and RMSE
Baseline Model Svm, supplied by Chennai Corporation, 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/baseline model svm/product/Chennai Corporation
Average 90 stars, based on 1 article reviews
baseline model svm - by Bioz Stars, 2026-03
90/100 stars
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svm model  (Prerna Laboratories)
90
Prerna Laboratories svm model
Comparison table of baseline models vs. our study using R and RMSE
Svm Model, supplied by Prerna Laboratories, 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 model/product/Prerna Laboratories
Average 90 stars, based on 1 article reviews
svm model - by Bioz Stars, 2026-03
90/100 stars
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Image Search Results


a.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the originally published LDA model in women without a psychiatric history. b.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model in women without a psychiatric history. c.) Plot of EPDS threshold values (x axis) as a function of the mean model output (predicted probability) for women above the EPDS threshold minus that for women below the threshold (y axis) for the originally published LDA model in women without a psychiatric history. d.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model in women with a previous history of PPD. e.) Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women with a previous history of PPD. f.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women without a psychiatric history. g.) Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. h.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. Horizontal dashed red lines denote an AUC of 70% while a dashed vertical black line denotes an EPDS of ≥ 13, signifying likely PPD.

Journal: Psychiatry research

Article Title: DNA methylation biomarkers prospectively predict both antenatal and postpartum depression

doi: 10.1016/j.psychres.2019.112711

Figure Lengend Snippet: a.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the originally published LDA model in women without a psychiatric history. b.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model in women without a psychiatric history. c.) Plot of EPDS threshold values (x axis) as a function of the mean model output (predicted probability) for women above the EPDS threshold minus that for women below the threshold (y axis) for the originally published LDA model in women without a psychiatric history. d.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model in women with a previous history of PPD. e.) Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women with a previous history of PPD. f.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the SVM model in women without a psychiatric history. g.) Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. h.)Plot of EPDS threshold values (x axis) as a function of the mean model output for women above the EPDS threshold minus that for women below the threshold (y axis) for the novelly SVM model accounting for antenatal depression status in a combined sample of women with and without a previous history of PPD. Horizontal dashed red lines denote an AUC of 70% while a dashed vertical black line denotes an EPDS of ≥ 13, signifying likely PPD.

Article Snippet: From a practical standpoint, in order to generate a model with the potential to be efficacious in a clinical environment, we trained a new SVM model on the Johns Hopkins Prospective PPD cohort data incorporating antenatal depression status as an interaction covariate in the model. We applied this to the combined UC Irvine cohort, inputting previous history of PPD as the interacting covariate and observed significant correlations of predictive accuracy with increasing depression severity (T1: Rho= 0.94, p = 0, T2: Rho= 0.91, p = 0, T3: Rho= 0.57, p = 0.013) with T3 samples achieving an AUC of 0.77 (95% CI: 0.64–0.77) to detect an EPDS ≥ 13 ( ).

Techniques:

a.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model accounting for antenatal depression status in women from the UC Irvine cohort where antenatal depression status is determined with T1 time point biomarker output. b.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model accounting for antenatal depression status in the JHU Prospective Neuroimaging cohort. Horizontal dashed red lines denote an AUC of 70% while a dashed vertical black line denotes an EPDS of ≥ 13, signifying likely PPD. c.) A plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for an SVM model to detect PPD status trained on a model incorporating variation in both TTC9B and HP1BP3 across three technical replicates in a subset of N = 20 women from the JHU Prospective Neuroimaging cohort. Horizontal dashed red lines denote an AUC of 80%.

Journal: Psychiatry research

Article Title: DNA methylation biomarkers prospectively predict both antenatal and postpartum depression

doi: 10.1016/j.psychres.2019.112711

Figure Lengend Snippet: a.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model accounting for antenatal depression status in women from the UC Irvine cohort where antenatal depression status is determined with T1 time point biomarker output. b.)Plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for the SVM model accounting for antenatal depression status in the JHU Prospective Neuroimaging cohort. Horizontal dashed red lines denote an AUC of 70% while a dashed vertical black line denotes an EPDS of ≥ 13, signifying likely PPD. c.) A plot of EPDS threshold values (x axis) as a function of the AUC of prediction for the number of women above that threshold (y axis) for an SVM model to detect PPD status trained on a model incorporating variation in both TTC9B and HP1BP3 across three technical replicates in a subset of N = 20 women from the JHU Prospective Neuroimaging cohort. Horizontal dashed red lines denote an AUC of 80%.

Article Snippet: From a practical standpoint, in order to generate a model with the potential to be efficacious in a clinical environment, we trained a new SVM model on the Johns Hopkins Prospective PPD cohort data incorporating antenatal depression status as an interaction covariate in the model. We applied this to the combined UC Irvine cohort, inputting previous history of PPD as the interacting covariate and observed significant correlations of predictive accuracy with increasing depression severity (T1: Rho= 0.94, p = 0, T2: Rho= 0.91, p = 0, T3: Rho= 0.57, p = 0.013) with T3 samples achieving an AUC of 0.77 (95% CI: 0.64–0.77) to detect an EPDS ≥ 13 ( ).

Techniques: Biomarker Discovery

Comparison table of baseline models vs. our study using R and RMSE

Journal: Environmental Monitoring and Assessment

Article Title: Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

doi: 10.1007/s10661-025-13738-8

Figure Lengend Snippet: Comparison table of baseline models vs. our study using R and RMSE

Article Snippet: Chennai , Baseline model , SVM , 0.9465 , 0.8083.

Techniques: Comparison