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partial least squares algorithm function in smartpls 3.27  (SmartPLS GmbH)

 
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    SmartPLS GmbH partial least squares algorithm function in smartpls 3.27
    Partial Least Squares Algorithm Function In Smartpls 3.27, supplied by SmartPLS GmbH, 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/partial+least-squares+algorithm/pmc12278142-232-10-10?v=SmartPLS+GmbH
    Average 90 stars, based on 1 article reviews
    partial least squares algorithm function in smartpls 3.27 - by Bioz Stars, 2026-06
    90/100 stars

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    Umetrics orthogonal partial least square (opls) algorithm
    Validation of predictive models for PDB severity scores. Orthogonal Partial Least Square <t>(OPLS)</t> classifier was trained on the discover set and tested on the cross-validation set. The features consisted of the list of significant sites from site and region level analysis based on the discovery samples (Panels A and C, n = 2,247 CpG sites) and separately, on a best explanatory subset from this pool of sites obtained with the Glmnet (Panels B and D, n = 33 CpG sites) (A,B) Scatter plots of predicted versus observed PDB_severity scores for each model. The observed PDB severity scores were also dichotomized into two groups based on the median PDB severity score of seven; low severity (PDB_severity_score <7; blue) and high severity (PDB_severity_score ≥7; red) and subsequently used for ROC curve analysis showing the area under curve AUC for pooled sites (C) and Glmnet selected sites (D) .
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    Validation of predictive models for PDB severity scores. Orthogonal Partial Least Square (OPLS) classifier was trained on the discover set and tested on the cross-validation set. The features consisted of the list of significant sites from site and region level analysis based on the discovery samples (Panels A and C, n = 2,247 CpG sites) and separately, on a best explanatory subset from this pool of sites obtained with the Glmnet (Panels B and D, n = 33 CpG sites) (A,B) Scatter plots of predicted versus observed PDB_severity scores for each model. The observed PDB severity scores were also dichotomized into two groups based on the median PDB severity score of seven; low severity (PDB_severity_score <7; blue) and high severity (PDB_severity_score ≥7; red) and subsequently used for ROC curve analysis showing the area under curve AUC for pooled sites (C) and Glmnet selected sites (D) .

    Journal: Frontiers in Cell and Developmental Biology

    Article Title: Epigenetic DNA Methylation Signatures Associated With the Severity of Paget’s Disease of Bone

    doi: 10.3389/fcell.2022.903612

    Figure Lengend Snippet: Validation of predictive models for PDB severity scores. Orthogonal Partial Least Square (OPLS) classifier was trained on the discover set and tested on the cross-validation set. The features consisted of the list of significant sites from site and region level analysis based on the discovery samples (Panels A and C, n = 2,247 CpG sites) and separately, on a best explanatory subset from this pool of sites obtained with the Glmnet (Panels B and D, n = 33 CpG sites) (A,B) Scatter plots of predicted versus observed PDB_severity scores for each model. The observed PDB severity scores were also dichotomized into two groups based on the median PDB severity score of seven; low severity (PDB_severity_score <7; blue) and high severity (PDB_severity_score ≥7; red) and subsequently used for ROC curve analysis showing the area under curve AUC for pooled sites (C) and Glmnet selected sites (D) .

    Article Snippet: The Orthogonal Partial Least Square (OPLS) algorithm by SIMCA ver.15 (Umetrics, Sweden) was used to construct predictive models.

    Techniques: Biomarker Discovery