Search alternatives:
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
all optimization » art optimization (Expand Search), ai optimization (Expand Search), whale optimization (Expand Search)
based function » based functional (Expand Search), basis function (Expand Search), basis functions (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
less based » lens based (Expand Search), lemos based (Expand Search), degs based (Expand Search)
based all » based small (Expand Search), based cell (Expand Search), based ap (Expand Search)
function optimization » reaction optimization (Expand Search), formulation optimization (Expand Search), generation optimization (Expand Search)
all optimization » art optimization (Expand Search), ai optimization (Expand Search), whale optimization (Expand Search)
based function » based functional (Expand Search), basis function (Expand Search), basis functions (Expand Search)
binary based » library based (Expand Search), linac based (Expand Search), binary mask (Expand Search)
less based » lens based (Expand Search), lemos based (Expand Search), degs based (Expand Search)
based all » based small (Expand Search), based cell (Expand Search), based ap (Expand Search)
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Descriptive statistics for variables.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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SHAP summary plot.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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ROC curves for the test set of four models.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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84
Display of the web prediction interface.
Published 2025“…</p><p>Results</p><p>Logistic regression analysis identified age, hemoglobin concentration, education level, and social participation as significant factors influencing CI. Models based on NNET, RF, LR, and SVM algorithms were developed, achieving AUC of 0.918, 0.889, 0.872, and 0.760, respectively, on the test set. …”
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Process of generating MS/MS spectra from a protein mixture using mass-spectrometry analysis.
Published 2021Subjects: -
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2D UMAP visualization of embedded spectra and peptide vectors generated by SpeCollate.
Published 2021Subjects: -
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Venn diagrams showing the overlap of peptides among SpeCollate, Crux, and MSFragger.
Published 2021Subjects: -
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