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Genovis Inc selection operator lasso binary logistic regression model
Selection Operator Lasso Binary Logistic 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+logistic+regression+model/OpeRATOR+Lyophilized/10__1097_slash_js9__0000000000003745-334-17-18
Average 93 stars, based on 92 article reviews

selection operator lasso binary logistic regression model - by Bioz Stars, 2026-10

93/100 stars

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Article Title: Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.
Article Snippet: .. To establish the model, we employed a least absolute shrinkage and selection operator (LASSO) logistic regression model, selecting only nonzero coefficients. ..

Article Title: Significance of integrated clinical and proteomic characteristics analysis for pathogenesis and management of Crohn's disease with concomitant psoriasis
Article Snippet: .. Feature selection for the faecal proteomics data was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression model. ..

Article Title: Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery.
Article Snippet: .. Harris et al., developed a model base spanning admissions from 2011–2017, and including 82,168 subjects using a ML based Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model. ..

Article Title: CT-Based Radiomics Features Combined with AFP for Predicting Vessels Encapsulating Tumor Clusters and Prognosis of Hepatocellular Carcinoma
Article Snippet: .. Subsequently, feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO)-logistic regression model. ..

Article Title: Significance of integrated clinical and proteomic characteristics analysis for pathogenesis and management of Crohn's disease with concomitant psoriasis.
Article Snippet: .. Lasso regression and support vector machine (SVM) model Feature selection for the faecal proteomics data was performed using the least absolute shrinkage and selection operator (LASSO) logistic regression model. ..

Article Title: Parsimonious and explainable machine learning for predicting mortality in patients post hip fracture surgery
Article Snippet: .. Harris et al., developed a model base spanning admissions from 2011–2017, and including 82,168 subjects using a ML based Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model. ..

Article Title: Driver mutations and malignant pleural effusion in non-small cell lung cancer.
Article Snippet: .. To identify independent predictors of pleural effusion, a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was applied. ..

Article Title: Driver mutations and malignant pleural effusion in non-small cell lung cancer
Article Snippet: A p-value < 0.05 was considered statistically significant. .. To identify independent predictors of pleural effusion, a Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression model was applied. ..

Image Search Results

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