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selection operator lasso regression model  (Genovis Inc)


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    Genovis Inc selection operator lasso regression model
    Selection Operator Lasso Regression Model, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 92 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/selection+operator+lasso+regression+model/OpeRATOR+Lyophilized/pmc12658097-134-10-11
    Average 93 stars, based on 92 article reviews
    selection operator lasso regression model - by Bioz Stars, 2026-09
    93/100 stars

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    Related Articles

    Selection:

    Article Title: Sysmex Cell Population Data for Diagnosing Infection in Patients With Suspected Sepsis in the Emergency Department.
    Article Snippet: Objectives: Early diagnosis of suspected sepsis is crucial to improve patient survival.. Cell population (CP) data, a set of leucocyte research parameters from hematology instruments, has a potential as markers for infection.. The aim of this study was to investigate the diagnostic accuracy for infection of CP variables from Sysmex XN instruments in patients with suspected sepsis in the emergency department (ED).

    Article Title: Transformer-based multimodal fusion model predicts early hematoma expansion in spontaneous cerebral hemorrhage: A multicenter study.
    Article Snippet: .. To further filter features, we constructed a feature signature using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model on the remaining features. ..

    Article Title: Development and validation of a nomogram for predicting 30-day mortality in patients with severe acute pancreatitis and pneumonia
    Article Snippet: .. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO) regression model with minimum log lambda, coupled with 10-fold cross-validation to reduce the risk of overfitting. ..

    Article Title: Dynamic nomogram for predicting depression risk in middle-aged and older adults based on CHARLS.
    Article Snippet: .. The process of feature selection using the least absolute shrinkage and selection operator (LASSO) regression model. (A) The optimal λ value is selected based on tenfold cross-validation and minimum mean squared error (MSE), with vertical dashed lines indicating the optimal λ value. (B) LASSO coefficients for different λ values, with vertical dashed lines indicating the number of features corresponding to the optimal λ value (9 features). (C) After feature selection using the LASSO regression, the features with nonzero coefficients are shown. ..

    Article Title: Dynamic nomogram for predicting depression risk in middle-aged and older adults based on CHARLS
    Article Snippet: .. Fig. 1 The process of feature selection using the least absolute shrinkage and selection operator (LASSO) regression model. ( A ) The optimal λ value is selected based on tenfold cross-validation and minimum mean squared error (MSE), with vertical dashed lines indicating the optimal λ value. ( B ) LASSO coefficients for different λ values, with vertical dashed lines indicating the number of features corresponding to the optimal λ value (9 features). ( C ) After feature selection using the LASSO regression, the features with nonzero coefficients are shown. ..

    Article Title: Development and validation of a CT-based comprehensive nomogram for differentiating benign from malignant subcentimeter solid nodules
    Article Snippet: .. The final feature set was determined using a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, with the optimal λ value selected via 10-fold cross-validation to minimize the mean squared error. (see Page 15-16, line 321- 330) A clinical model was constructed based on independent risk factors identified through univariate and multivariate logistic regression analysis. (see Page 16, line 337-339) Comment 6: In statistics, the authors need to describe the calculation of sensitivity and specificity of the nomogram because this model is developed for the differential diagnosis for benign and malignant SSPNs. ..

    Article Title: TRAF3IP2 as a novel inflammatory biomarker for coronary artery disease: development and validation of a multimodal prediction model
    Article Snippet: .. In this study, the λ _min value (0.09401152) of the vertical dashed line on the right side of the LASSO regression curve was chosen as the optimal model. By using the least absolute shrinkage and selection operator (LASSO) regression model, we identified 9 risk factors, namely, age, sex, smoking history, diabetes history, serum creatinine level, phosphoremia, total cholesterol (TC) level, triglyceride (TG) level, and TRAF3IP2 expression. ..

    Infection:

    Article Title: Sysmex Cell Population Data for Diagnosing Infection in Patients With Suspected Sepsis in the Emergency Department.
    Article Snippet: Objectives: Early diagnosis of suspected sepsis is crucial to improve patient survival.. Cell population (CP) data, a set of leucocyte research parameters from hematology instruments, has a potential as markers for infection.. The aim of this study was to investigate the diagnostic accuracy for infection of CP variables from Sysmex XN instruments in patients with suspected sepsis in the emergency department (ED).

    Construct:

    Article Title: Transformer-based multimodal fusion model predicts early hematoma expansion in spontaneous cerebral hemorrhage: A multicenter study.
    Article Snippet: .. To further filter features, we constructed a feature signature using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model on the remaining features. ..

    Article Title: Development and validation of a CT-based comprehensive nomogram for differentiating benign from malignant subcentimeter solid nodules
    Article Snippet: .. The final feature set was determined using a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, with the optimal λ value selected via 10-fold cross-validation to minimize the mean squared error. (see Page 15-16, line 321- 330) A clinical model was constructed based on independent risk factors identified through univariate and multivariate logistic regression analysis. (see Page 16, line 337-339) Comment 6: In statistics, the authors need to describe the calculation of sensitivity and specificity of the nomogram because this model is developed for the differential diagnosis for benign and malignant SSPNs. ..

    Biomarker Discovery:

    Article Title: Development and validation of a CT-based comprehensive nomogram for differentiating benign from malignant subcentimeter solid nodules
    Article Snippet: .. The final feature set was determined using a Least Absolute Shrinkage and Selection Operator (LASSO) regression model, with the optimal λ value selected via 10-fold cross-validation to minimize the mean squared error. (see Page 15-16, line 321- 330) A clinical model was constructed based on independent risk factors identified through univariate and multivariate logistic regression analysis. (see Page 16, line 337-339) Comment 6: In statistics, the authors need to describe the calculation of sensitivity and specificity of the nomogram because this model is developed for the differential diagnosis for benign and malignant SSPNs. ..

    Expressing:

    Article Title: TRAF3IP2 as a novel inflammatory biomarker for coronary artery disease: development and validation of a multimodal prediction model
    Article Snippet: .. In this study, the λ _min value (0.09401152) of the vertical dashed line on the right side of the LASSO regression curve was chosen as the optimal model. By using the least absolute shrinkage and selection operator (LASSO) regression model, we identified 9 risk factors, namely, age, sex, smoking history, diabetes history, serum creatinine level, phosphoremia, total cholesterol (TC) level, triglyceride (TG) level, and TRAF3IP2 expression. ..



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    Prediction of CDPS and the correlation <t>between</t> <t>proteomics</t> and clinical characteristics. A . Heatmap of the correlation among expression of co-expression protein modules, expression of CDPS key proteins and clinical characteristics (IFX, response to infliximab; UST, response to ustekinumab; PS, psoriasis; NEUT, neutrophils; CRP, C-reactive protein; WBC, white blood cell; ESR, erythrocyte sedimentation rate; ALB, albumin; SES-CD, simple endoscopic score for Crohn's Disease); ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. B . Heatmap of correlation between immune cell infiltration and expression of co-expression modules and CDPS key proteins; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. C . Box plot of expression of co-expression modules in response (R) and non-response (NR) group (UST, ustekinumab; IFX, infliximab); UST (NR) n = 6, UST (R) n = 11; IFX (NR) n = 13, IFX (R) n = 6, Wilcoxon rank-sum test, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. D . Box plot of level of immune cells infiltration in response (R) and non-response (NR) group (UST: ustekinumab; IFX, infliximab); UST (NR) n = 6, UST (R) n = 11; IFX (NR) n = 13, IFX (R) n = 6, Wilcoxon rank-sum test, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. E . Heatmap of correlation between immune cells infiltration and each of the following: response to ustekinumab, response to infliximab, and the occurrence of psoriasis (IFX, response to infliximab; UST, response to ustekinumab; PS, psoriasis); ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. F . ROC curves of prediction models (validation group) for the likelihood of achieving an endoscopic response by intestinal mucosa proteomics; activated CD8 T cell AUC: 0.675 (95% CI: 0.333–1.000), turquoise AUC: 0.750 (95% CI: 0.500–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000). G . ROC curves of prediction models (validation group) for the likelihood of achieving an endoscopic response by body fluids proteomics; HGFAC (plasma) AUC: 0.780 (95% CI: 0.333–1.000), HGFAC (urine) AUC: 0.780 (95% CI: 0.333–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000). H . ROC curves of prediction models (validation group) for likelihood of the development of CDPS constructed using selected features by <t>lasso</t> regression; turquoise AUC: 0.960 (95% CI: 0.750–1.000), KRT7 AUC: 0.622 (95% CI: 0.333–1.000), HGFAC (plasma) AUC: 0.750 (95% CI: 0.500–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000).
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    Prediction of CDPS and the correlation between proteomics and clinical characteristics. A . Heatmap of the correlation among expression of co-expression protein modules, expression of CDPS key proteins and clinical characteristics (IFX, response to infliximab; UST, response to ustekinumab; PS, psoriasis; NEUT, neutrophils; CRP, C-reactive protein; WBC, white blood cell; ESR, erythrocyte sedimentation rate; ALB, albumin; SES-CD, simple endoscopic score for Crohn's Disease); ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. B . Heatmap of correlation between immune cell infiltration and expression of co-expression modules and CDPS key proteins; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. C . Box plot of expression of co-expression modules in response (R) and non-response (NR) group (UST, ustekinumab; IFX, infliximab); UST (NR) n = 6, UST (R) n = 11; IFX (NR) n = 13, IFX (R) n = 6, Wilcoxon rank-sum test, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. D . Box plot of level of immune cells infiltration in response (R) and non-response (NR) group (UST: ustekinumab; IFX, infliximab); UST (NR) n = 6, UST (R) n = 11; IFX (NR) n = 13, IFX (R) n = 6, Wilcoxon rank-sum test, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. E . Heatmap of correlation between immune cells infiltration and each of the following: response to ustekinumab, response to infliximab, and the occurrence of psoriasis (IFX, response to infliximab; UST, response to ustekinumab; PS, psoriasis); ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. F . ROC curves of prediction models (validation group) for the likelihood of achieving an endoscopic response by intestinal mucosa proteomics; activated CD8 T cell AUC: 0.675 (95% CI: 0.333–1.000), turquoise AUC: 0.750 (95% CI: 0.500–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000). G . ROC curves of prediction models (validation group) for the likelihood of achieving an endoscopic response by body fluids proteomics; HGFAC (plasma) AUC: 0.780 (95% CI: 0.333–1.000), HGFAC (urine) AUC: 0.780 (95% CI: 0.333–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000). H . ROC curves of prediction models (validation group) for likelihood of the development of CDPS constructed using selected features by lasso regression; turquoise AUC: 0.960 (95% CI: 0.750–1.000), KRT7 AUC: 0.622 (95% CI: 0.333–1.000), HGFAC (plasma) AUC: 0.750 (95% CI: 0.500–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000).

    Journal: eBioMedicine

    Article Title: Significance of integrated clinical and proteomic characteristics analysis for pathogenesis and management of Crohn's disease with concomitant psoriasis

    doi: 10.1016/j.ebiom.2025.105981

    Figure Lengend Snippet: Prediction of CDPS and the correlation between proteomics and clinical characteristics. A . Heatmap of the correlation among expression of co-expression protein modules, expression of CDPS key proteins and clinical characteristics (IFX, response to infliximab; UST, response to ustekinumab; PS, psoriasis; NEUT, neutrophils; CRP, C-reactive protein; WBC, white blood cell; ESR, erythrocyte sedimentation rate; ALB, albumin; SES-CD, simple endoscopic score for Crohn's Disease); ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. B . Heatmap of correlation between immune cell infiltration and expression of co-expression modules and CDPS key proteins; ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. C . Box plot of expression of co-expression modules in response (R) and non-response (NR) group (UST, ustekinumab; IFX, infliximab); UST (NR) n = 6, UST (R) n = 11; IFX (NR) n = 13, IFX (R) n = 6, Wilcoxon rank-sum test, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. D . Box plot of level of immune cells infiltration in response (R) and non-response (NR) group (UST: ustekinumab; IFX, infliximab); UST (NR) n = 6, UST (R) n = 11; IFX (NR) n = 13, IFX (R) n = 6, Wilcoxon rank-sum test, ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. E . Heatmap of correlation between immune cells infiltration and each of the following: response to ustekinumab, response to infliximab, and the occurrence of psoriasis (IFX, response to infliximab; UST, response to ustekinumab; PS, psoriasis); ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001. F . ROC curves of prediction models (validation group) for the likelihood of achieving an endoscopic response by intestinal mucosa proteomics; activated CD8 T cell AUC: 0.675 (95% CI: 0.333–1.000), turquoise AUC: 0.750 (95% CI: 0.500–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000). G . ROC curves of prediction models (validation group) for the likelihood of achieving an endoscopic response by body fluids proteomics; HGFAC (plasma) AUC: 0.780 (95% CI: 0.333–1.000), HGFAC (urine) AUC: 0.780 (95% CI: 0.333–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000). H . ROC curves of prediction models (validation group) for likelihood of the development of CDPS constructed using selected features by lasso regression; turquoise AUC: 0.960 (95% CI: 0.750–1.000), KRT7 AUC: 0.622 (95% CI: 0.333–1.000), HGFAC (plasma) AUC: 0.750 (95% CI: 0.500–1.000), multi-omics model AUC: 0.830 (95% CI: 0.500–1.000).

    Article Snippet: Feature selection for the faecal proteomics data was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression model.

    Techniques: Expressing, Sedimentation, Biomarker Discovery, Clinical Proteomics, Construct