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vs-svm  (MathWorks Inc)


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    MathWorks Inc vs-svm
    Vs 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/product/vs-svm/pm37466636-300-7-10
    Average 90 stars, based on 1 article reviews
    vs-svm - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Open-Source Machine Learning in Computational Chemistry.
    Article Snippet: The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms.. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches.. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available.



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    The proposed multi-model framework for machine learning (ML) based fibrosis staging. This approach will provide a fibrosis staging between 0 to 100 regardless of vendor. In this work we only tested ultrasound elastography shear wave velocity (USE SWV) measurements obtained using Siemens and Philips scanners, with magnetic resonance elastography (MRE) as ground truth. However, in the future this model could be extended to other vendors after additional training and validation on those datasets.

    Journal: Ultrasound in medicine & biology

    Article Title: A New Multi-Model Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

    doi: 10.1016/j.ultrasmedbio.2019.09.004

    Figure Lengend Snippet: The proposed multi-model framework for machine learning (ML) based fibrosis staging. This approach will provide a fibrosis staging between 0 to 100 regardless of vendor. In this work we only tested ultrasound elastography shear wave velocity (USE SWV) measurements obtained using Siemens and Philips scanners, with magnetic resonance elastography (MRE) as ground truth. However, in the future this model could be extended to other vendors after additional training and validation on those datasets.

    Article Snippet: Most notably, the difference in AUC between median shear wave velocity and SVM was statistically significant for both Siemens and Philips, although the p-value was better for Siemens as it had a larger sample size ( ). table ft1 table-wrap mode="anchored" t5 Table 5. caption a7 p-value Significantly Different Siemens - Median SWV vs. SVM 4.95E-05 Yes Siemens - Median SWV vs. QDA 0.19098 No Siemens - Median SWV vs. Bayesian 0.46593 No Siemens - Median SWV vs. GLRM 0.19098 No Philips - Median SWV vs. SVM 0.036085 Yes Philips - Median SWV vs. QDA 0.32787 No Philips - Median SWV vs. Bayesian 0.22957 No Philips - Median SWV vs. GLRM 0.71877 No Open in a separate window AUC = area-under-the-curve, GLRM = generalized linear regression model, QDA = quadratic discriminant analysis, SVM = support vector machine, SWV = shear wave velocity.

    Techniques: Shear, Biomarker Discovery

    Performance of each machine learning algorithm as well as median shear wave velocity in predicting clinically non-significant versus significant fibrosis in the Group 1 dataset (pSWE).

    Journal: Ultrasound in medicine & biology

    Article Title: A New Multi-Model Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

    doi: 10.1016/j.ultrasmedbio.2019.09.004

    Figure Lengend Snippet: Performance of each machine learning algorithm as well as median shear wave velocity in predicting clinically non-significant versus significant fibrosis in the Group 1 dataset (pSWE).

    Article Snippet: Most notably, the difference in AUC between median shear wave velocity and SVM was statistically significant for both Siemens and Philips, although the p-value was better for Siemens as it had a larger sample size ( ). table ft1 table-wrap mode="anchored" t5 Table 5. caption a7 p-value Significantly Different Siemens - Median SWV vs. SVM 4.95E-05 Yes Siemens - Median SWV vs. QDA 0.19098 No Siemens - Median SWV vs. Bayesian 0.46593 No Siemens - Median SWV vs. GLRM 0.19098 No Philips - Median SWV vs. SVM 0.036085 Yes Philips - Median SWV vs. QDA 0.32787 No Philips - Median SWV vs. Bayesian 0.22957 No Philips - Median SWV vs. GLRM 0.71877 No Open in a separate window AUC = area-under-the-curve, GLRM = generalized linear regression model, QDA = quadratic discriminant analysis, SVM = support vector machine, SWV = shear wave velocity.

    Techniques: Shear

    Performance of each machine learning algorithm as well as median shear wave velocity (without cutoff value) in predicting clinically non-significant versus significant fibrosis in Group 2 dataset (2DSWE).

    Journal: Ultrasound in medicine & biology

    Article Title: A New Multi-Model Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

    doi: 10.1016/j.ultrasmedbio.2019.09.004

    Figure Lengend Snippet: Performance of each machine learning algorithm as well as median shear wave velocity (without cutoff value) in predicting clinically non-significant versus significant fibrosis in Group 2 dataset (2DSWE).

    Article Snippet: Most notably, the difference in AUC between median shear wave velocity and SVM was statistically significant for both Siemens and Philips, although the p-value was better for Siemens as it had a larger sample size ( ). table ft1 table-wrap mode="anchored" t5 Table 5. caption a7 p-value Significantly Different Siemens - Median SWV vs. SVM 4.95E-05 Yes Siemens - Median SWV vs. QDA 0.19098 No Siemens - Median SWV vs. Bayesian 0.46593 No Siemens - Median SWV vs. GLRM 0.19098 No Philips - Median SWV vs. SVM 0.036085 Yes Philips - Median SWV vs. QDA 0.32787 No Philips - Median SWV vs. Bayesian 0.22957 No Philips - Median SWV vs. GLRM 0.71877 No Open in a separate window AUC = area-under-the-curve, GLRM = generalized linear regression model, QDA = quadratic discriminant analysis, SVM = support vector machine, SWV = shear wave velocity.

    Techniques: Shear

    Difference in AUC between the ML algorithm SVM and median SWV was statistically significant for both groups.

    Journal: Ultrasound in medicine & biology

    Article Title: A New Multi-Model Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

    doi: 10.1016/j.ultrasmedbio.2019.09.004

    Figure Lengend Snippet: Difference in AUC between the ML algorithm SVM and median SWV was statistically significant for both groups.

    Article Snippet: Most notably, the difference in AUC between median shear wave velocity and SVM was statistically significant for both Siemens and Philips, although the p-value was better for Siemens as it had a larger sample size ( ). table ft1 table-wrap mode="anchored" t5 Table 5. caption a7 p-value Significantly Different Siemens - Median SWV vs. SVM 4.95E-05 Yes Siemens - Median SWV vs. QDA 0.19098 No Siemens - Median SWV vs. Bayesian 0.46593 No Siemens - Median SWV vs. GLRM 0.19098 No Philips - Median SWV vs. SVM 0.036085 Yes Philips - Median SWV vs. QDA 0.32787 No Philips - Median SWV vs. Bayesian 0.22957 No Philips - Median SWV vs. GLRM 0.71877 No Open in a separate window AUC = area-under-the-curve, GLRM = generalized linear regression model, QDA = quadratic discriminant analysis, SVM = support vector machine, SWV = shear wave velocity.

    Techniques:

    Scores for non-significant and significant fibrosis separation using median shear wave velocity (SWV) as well as the new machine learning (ML) algorithms in dataset 1 (a. pSWE), and dataset 2 (b. 2DSWE). The different scores reflect the likelihood that the label came from each class (non-significant or significant fibrosis). Boxplots show excellent score separation in both datasets when a support vector machine (SVM) is used to perform classification, compared to worse score separation with median SWV. Note that ML scores differ between systems from different vendors as well as for the different ML algorithms.

    Journal: Ultrasound in medicine & biology

    Article Title: A New Multi-Model Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors

    doi: 10.1016/j.ultrasmedbio.2019.09.004

    Figure Lengend Snippet: Scores for non-significant and significant fibrosis separation using median shear wave velocity (SWV) as well as the new machine learning (ML) algorithms in dataset 1 (a. pSWE), and dataset 2 (b. 2DSWE). The different scores reflect the likelihood that the label came from each class (non-significant or significant fibrosis). Boxplots show excellent score separation in both datasets when a support vector machine (SVM) is used to perform classification, compared to worse score separation with median SWV. Note that ML scores differ between systems from different vendors as well as for the different ML algorithms.

    Article Snippet: Most notably, the difference in AUC between median shear wave velocity and SVM was statistically significant for both Siemens and Philips, although the p-value was better for Siemens as it had a larger sample size ( ). table ft1 table-wrap mode="anchored" t5 Table 5. caption a7 p-value Significantly Different Siemens - Median SWV vs. SVM 4.95E-05 Yes Siemens - Median SWV vs. QDA 0.19098 No Siemens - Median SWV vs. Bayesian 0.46593 No Siemens - Median SWV vs. GLRM 0.19098 No Philips - Median SWV vs. SVM 0.036085 Yes Philips - Median SWV vs. QDA 0.32787 No Philips - Median SWV vs. Bayesian 0.22957 No Philips - Median SWV vs. GLRM 0.71877 No Open in a separate window AUC = area-under-the-curve, GLRM = generalized linear regression model, QDA = quadratic discriminant analysis, SVM = support vector machine, SWV = shear wave velocity.

    Techniques: Shear, Plasmid Preparation