Review





Similar Products

selection operator lasso logistic regression algorithm  (Genovis Inc)
93
Genovis Inc selection operator lasso logistic regression algorithm
Selection Operator Lasso Logistic Regression Algorithm, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/logistic+lasso+regression/OpeRATOR+Lyophilized/pm41275152-51-22-23
Average 93 stars, based on 1 article reviews
selection operator lasso logistic regression algorithm - by Bioz Stars, 2026-09
93/100 stars
  Buy from Supplier
selection operator lasso logistic regression  (Genovis Inc)
93
Genovis Inc selection operator lasso logistic regression
Identification of <t>key</t> <t>mitochondrial-related</t> DEGs by 5 machine learning methods (A) Screening of key mitochondrial-related DEGs via Least Absolute Shrinkage and Selection Operator <t>(LASSO)</t> logistic regression. (B) Key mitochondrial-related DEGs identified using the Support Vector Machine (SVM) algorithm. (C) Decision Tree algorithm-based identification of key mitochondrial-related DEGs. (D) Key mitochondrial-related DEGs identified through the Random Forest algorithm. (E) Screening of key mitochondrial-related DEGs using the Boruta algorithm.
Selection Operator Lasso Logistic Regression, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/logistic+lasso+regression/OpeRATOR+Lyophilized/pmc12552154-167-21-22
Average 93 stars, based on 1 article reviews
selection operator lasso logistic regression - by Bioz Stars, 2026-09
93/100 stars
  Buy from Supplier
selection operator lasso 320 logistic regression  (Genovis Inc)
93
Genovis Inc selection operator lasso 320 logistic regression
Identification of <t>key</t> <t>mitochondrial-related</t> DEGs by 5 machine learning methods (A) Screening of key mitochondrial-related DEGs via Least Absolute Shrinkage and Selection Operator <t>(LASSO)</t> logistic regression. (B) Key mitochondrial-related DEGs identified using the Support Vector Machine (SVM) algorithm. (C) Decision Tree algorithm-based identification of key mitochondrial-related DEGs. (D) Key mitochondrial-related DEGs identified through the Random Forest algorithm. (E) Screening of key mitochondrial-related DEGs using the Boruta algorithm.
Selection Operator Lasso 320 Logistic Regression, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/logistic+lasso+regression/OpeRATOR+Lyophilized/pm41270984-179-11-12
Average 93 stars, based on 1 article reviews
selection operator lasso 320 logistic regression - by Bioz Stars, 2026-09
93/100 stars
  Buy from Supplier
selection operator lasso binary logistic regression model  (Genovis Inc)
93
Genovis Inc selection operator lasso binary logistic regression model
Identification of <t>key</t> <t>mitochondrial-related</t> DEGs by 5 machine learning methods (A) Screening of key mitochondrial-related DEGs via Least Absolute Shrinkage and Selection Operator <t>(LASSO)</t> logistic regression. (B) Key mitochondrial-related DEGs identified using the Support Vector Machine (SVM) algorithm. (C) Decision Tree algorithm-based identification of key mitochondrial-related DEGs. (D) Key mitochondrial-related DEGs identified through the Random Forest algorithm. (E) Screening of key mitochondrial-related DEGs using the Boruta algorithm.
Selection Operator Lasso Binary Logistic Regression Model, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/logistic+lasso+regression/OpeRATOR+Lyophilized/10__1097_slash_js9__0000000000003745-334-17-18
Average 93 stars, based on 1 article reviews
selection operator lasso binary logistic regression model - by Bioz Stars, 2026-09
93/100 stars
  Buy from Supplier
selection operator lasso logistic regression model  (Genovis Inc)
93
Genovis Inc selection operator lasso logistic regression model
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).
Selection Operator Lasso Logistic Regression Model, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/logistic+lasso+regression/OpeRATOR+Lyophilized/pmc12616020-199-15-16
Average 93 stars, based on 1 article reviews
selection operator lasso logistic regression model - by Bioz Stars, 2026-09
93/100 stars
  Buy from Supplier
logistic regression with lasso  (Genovis Inc)
93
Genovis Inc logistic regression with lasso
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).
Logistic Regression With Lasso, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/logistic+lasso+regression/OpeRATOR+Lyophilized/pm41111097-64-27-36
Average 93 stars, based on 1 article reviews
logistic regression with lasso - by Bioz Stars, 2026-09
93/100 stars
  Buy from Supplier

Image Search Results


Identification of key mitochondrial-related DEGs by 5 machine learning methods (A) Screening of key mitochondrial-related DEGs via Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. (B) Key mitochondrial-related DEGs identified using the Support Vector Machine (SVM) algorithm. (C) Decision Tree algorithm-based identification of key mitochondrial-related DEGs. (D) Key mitochondrial-related DEGs identified through the Random Forest algorithm. (E) Screening of key mitochondrial-related DEGs using the Boruta algorithm.

Journal: iScience

Article Title: Interplay between acute Type A aortic dissection and pan-cancer: Clinical evidence, bioinformatics, and experimental validation

doi: 10.1016/j.isci.2025.113664

Figure Lengend Snippet: Identification of key mitochondrial-related DEGs by 5 machine learning methods (A) Screening of key mitochondrial-related DEGs via Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. (B) Key mitochondrial-related DEGs identified using the Support Vector Machine (SVM) algorithm. (C) Decision Tree algorithm-based identification of key mitochondrial-related DEGs. (D) Key mitochondrial-related DEGs identified through the Random Forest algorithm. (E) Screening of key mitochondrial-related DEGs using the Boruta algorithm.

Article Snippet: Identification of key mitochondrial-related DEGs by 5 machine learning methods (A) Screening of key mitochondrial-related DEGs via Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. (B) Key mitochondrial-related DEGs identified using the Support Vector Machine (SVM) algorithm. (C) Decision Tree algorithm-based identification of key mitochondrial-related DEGs. (D) Key mitochondrial-related DEGs identified through the Random Forest algorithm. (E) Screening of key mitochondrial-related DEGs using the Boruta algorithm.

Techniques: Selection, Plasmid Preparation

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