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pls-da machine learning models  (MathWorks Inc)


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    MathWorks Inc pls-da machine learning models
    Pls Da Machine Learning Models, 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/pls-da machine learning models/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    pls-da machine learning models - by Bioz Stars, 2026-06
    90/100 stars

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    Multivariate analysis of urinary ¹H NMR metabolomic profiles across groups: Primary Mitochondrial Disease (PMD, orange), Suspected Mitochondrial Disease (SMD, yellow), Control individuals (green), Chronic Kidney Disease (CKD) stages 1–2 (lilac) and stages 3–5 (purple). The ellipses represent 95% confidence intervals. A Principal Component Analysis (PCA) highlights distinct metabolic fingerprint in CKD patients compared to other groups. B Corresponding supervised model using partial least squares discriminant analysis (PLS-DA) shows metabolic divergence between groups, particularly between PMD, CKD3-5, and controls. PMD primary mitochondrial disease, SMD suspected mitochondrial dysfunction, CKD chronic kidney disease

    Journal: Metabolomics

    Article Title: NMR-based urinary biomarkers in pediatric primary mitochondrial disorders and chronic kidney disease: shared mitochondrial dysfunction, diverging biosignatures

    doi: 10.1007/s11306-025-02363-8

    Figure Lengend Snippet: Multivariate analysis of urinary ¹H NMR metabolomic profiles across groups: Primary Mitochondrial Disease (PMD, orange), Suspected Mitochondrial Disease (SMD, yellow), Control individuals (green), Chronic Kidney Disease (CKD) stages 1–2 (lilac) and stages 3–5 (purple). The ellipses represent 95% confidence intervals. A Principal Component Analysis (PCA) highlights distinct metabolic fingerprint in CKD patients compared to other groups. B Corresponding supervised model using partial least squares discriminant analysis (PLS-DA) shows metabolic divergence between groups, particularly between PMD, CKD3-5, and controls. PMD primary mitochondrial disease, SMD suspected mitochondrial dysfunction, CKD chronic kidney disease

    Article Snippet: Multivariate analysis (MVA) trough Principal component analysis (PCA) and partial-least-squares discriminant analysis (PLS-DA) models (SIMCA-P 11.5, Umetrics, Umeå, Sweden), were performed on full resolution 1 H NMR spectra, after unit variance (UV) scaling of the spectra.

    Techniques: Metabolomic, Control

    Supervised multivariate models and spectral features associated with mitochondrial disorders. I. Partial Least Squares Discriminant Analysis (PLS-DA) models were generated to compare patients with PMD to: A controls, B SMD, C . CKD stages 1–2, and D . CKD stages 3–5. The corresponding Q² values, indicate the predictive performance of each model. Clear discrimination was observed between PMD and controls (Q² = 0.53), and both CKD subgroups (Q² = 0.62 and 0.78). In contrast, the model comparing PMD to SMD showed lower predictive performance (Q² = 0.25), reflecting significant metabolic overlap. II. Corresponding loadings plot of model A, color-coded by variable importance in projection (VIP) scores for the first latent variable (LV1) highlighting spectral regions that contribute most to the separation between PMD and controls. (regions around 1.2–2.5 ppm and 3.0–4.5 ppm, consistent with metabolites involved in amino acid metabolism, organic acids, and energy-related pathways). Three-letter code used for amino acids. Abbreviations: 3-HBA: 3-hydroxybutyrate; 4-HPA: 4-hydroxyphenylacetate; N -PhAcGly: N -phenylacetylglycine. Ui: resonances at chemical shift i, which remain unassigned (or with tentative assignment)

    Journal: Metabolomics

    Article Title: NMR-based urinary biomarkers in pediatric primary mitochondrial disorders and chronic kidney disease: shared mitochondrial dysfunction, diverging biosignatures

    doi: 10.1007/s11306-025-02363-8

    Figure Lengend Snippet: Supervised multivariate models and spectral features associated with mitochondrial disorders. I. Partial Least Squares Discriminant Analysis (PLS-DA) models were generated to compare patients with PMD to: A controls, B SMD, C . CKD stages 1–2, and D . CKD stages 3–5. The corresponding Q² values, indicate the predictive performance of each model. Clear discrimination was observed between PMD and controls (Q² = 0.53), and both CKD subgroups (Q² = 0.62 and 0.78). In contrast, the model comparing PMD to SMD showed lower predictive performance (Q² = 0.25), reflecting significant metabolic overlap. II. Corresponding loadings plot of model A, color-coded by variable importance in projection (VIP) scores for the first latent variable (LV1) highlighting spectral regions that contribute most to the separation between PMD and controls. (regions around 1.2–2.5 ppm and 3.0–4.5 ppm, consistent with metabolites involved in amino acid metabolism, organic acids, and energy-related pathways). Three-letter code used for amino acids. Abbreviations: 3-HBA: 3-hydroxybutyrate; 4-HPA: 4-hydroxyphenylacetate; N -PhAcGly: N -phenylacetylglycine. Ui: resonances at chemical shift i, which remain unassigned (or with tentative assignment)

    Article Snippet: Multivariate analysis (MVA) trough Principal component analysis (PCA) and partial-least-squares discriminant analysis (PLS-DA) models (SIMCA-P 11.5, Umetrics, Umeå, Sweden), were performed on full resolution 1 H NMR spectra, after unit variance (UV) scaling of the spectra.

    Techniques: Generated