nonlinear regression analysis, log(inhibitor) vs. response – variable slope (four parameters) Search Results


version 16 1  (STATA Corporation)
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cem-matched analysis  (CEM Corporation)
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COVID-19 vaccine efficacy and safety – comparative studies.
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anti-s1 iga  (EUROIMMUN)
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COVID-19 vaccine efficacy and safety – comparative studies.
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neutralizing ab's  (GenScript corporation)
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COVID-19 vaccine efficacy and safety – comparative studies.
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unigene unigene0015748  (Unigene)
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COVID-19 vaccine efficacy and safety – comparative studies.
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networkanalyst  (Epigenomics ag)
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Epigenomics ag networkanalyst
Multi-omics integration to reduce overfitting by identifying of more biologically relevant features. (A) Comparison of the performance of a multi-omics model (DIABLO) to that of single-omics models of equivalent complexities, fit separately to individual omics datasets (otu, operating taxonomic units of the fecal microbiome; cpg, blood-based DNA methylation; protein, plasma proteomics; flow, cell counts by flow cytometry; mrna, whole blood transcriptomics). Mean squared error (MSE) differed significantly across all models (Kruskal-Wallis test; p = 0.0023), with the multi-omics model achieving significantly lower error (and better performance) when compared to all other models, with the exception of the proteomics-derived model (p = 0.068). (B) Integrated minimally-connected first-order network of features identified by DIABLO from transcriptomic, proteomics, and epigenetic data. Novel nodes identified from integration are highlighted in orange. (C) Selected enriched pathways and (D–F) selected enriched genes (mRNA) or proteins (proteomics) identified from integration <t>(NetworkAnalyst,</t> DIABLO, or both methods) are shown.
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nta—nanosight  (NanoSight ltd)
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Multi-omics integration to reduce overfitting by identifying of more biologically relevant features. (A) Comparison of the performance of a multi-omics model (DIABLO) to that of single-omics models of equivalent complexities, fit separately to individual omics datasets (otu, operating taxonomic units of the fecal microbiome; cpg, blood-based DNA methylation; protein, plasma proteomics; flow, cell counts by flow cytometry; mrna, whole blood transcriptomics). Mean squared error (MSE) differed significantly across all models (Kruskal-Wallis test; p = 0.0023), with the multi-omics model achieving significantly lower error (and better performance) when compared to all other models, with the exception of the proteomics-derived model (p = 0.068). (B) Integrated minimally-connected first-order network of features identified by DIABLO from transcriptomic, proteomics, and epigenetic data. Novel nodes identified from integration are highlighted in orange. (C) Selected enriched pathways and (D–F) selected enriched genes (mRNA) or proteins (proteomics) identified from integration <t>(NetworkAnalyst,</t> DIABLO, or both methods) are shown.
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hépar  (DuPont de Nemours)
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DuPont de Nemours hépar
Multi-omics integration to reduce overfitting by identifying of more biologically relevant features. (A) Comparison of the performance of a multi-omics model (DIABLO) to that of single-omics models of equivalent complexities, fit separately to individual omics datasets (otu, operating taxonomic units of the fecal microbiome; cpg, blood-based DNA methylation; protein, plasma proteomics; flow, cell counts by flow cytometry; mrna, whole blood transcriptomics). Mean squared error (MSE) differed significantly across all models (Kruskal-Wallis test; p = 0.0023), with the multi-omics model achieving significantly lower error (and better performance) when compared to all other models, with the exception of the proteomics-derived model (p = 0.068). (B) Integrated minimally-connected first-order network of features identified by DIABLO from transcriptomic, proteomics, and epigenetic data. Novel nodes identified from integration are highlighted in orange. (C) Selected enriched pathways and (D–F) selected enriched genes (mRNA) or proteins (proteomics) identified from integration <t>(NetworkAnalyst,</t> DIABLO, or both methods) are shown.
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array studio software  (OmicSoft Corporation)
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OmicSoft Corporation array studio software
Multi-omics integration to reduce overfitting by identifying of more biologically relevant features. (A) Comparison of the performance of a multi-omics model (DIABLO) to that of single-omics models of equivalent complexities, fit separately to individual omics datasets (otu, operating taxonomic units of the fecal microbiome; cpg, blood-based DNA methylation; protein, plasma proteomics; flow, cell counts by flow cytometry; mrna, whole blood transcriptomics). Mean squared error (MSE) differed significantly across all models (Kruskal-Wallis test; p = 0.0023), with the multi-omics model achieving significantly lower error (and better performance) when compared to all other models, with the exception of the proteomics-derived model (p = 0.068). (B) Integrated minimally-connected first-order network of features identified by DIABLO from transcriptomic, proteomics, and epigenetic data. Novel nodes identified from integration are highlighted in orange. (C) Selected enriched pathways and (D–F) selected enriched genes (mRNA) or proteins (proteomics) identified from integration <t>(NetworkAnalyst,</t> DIABLO, or both methods) are shown.
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radar ctdna detection assay  (NeoGenomics)
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NeoGenomics radar ctdna detection assay
Multi-omics integration to reduce overfitting by identifying of more biologically relevant features. (A) Comparison of the performance of a multi-omics model (DIABLO) to that of single-omics models of equivalent complexities, fit separately to individual omics datasets (otu, operating taxonomic units of the fecal microbiome; cpg, blood-based DNA methylation; protein, plasma proteomics; flow, cell counts by flow cytometry; mrna, whole blood transcriptomics). Mean squared error (MSE) differed significantly across all models (Kruskal-Wallis test; p = 0.0023), with the multi-omics model achieving significantly lower error (and better performance) when compared to all other models, with the exception of the proteomics-derived model (p = 0.068). (B) Integrated minimally-connected first-order network of features identified by DIABLO from transcriptomic, proteomics, and epigenetic data. Novel nodes identified from integration are highlighted in orange. (C) Selected enriched pathways and (D–F) selected enriched genes (mRNA) or proteins (proteomics) identified from integration <t>(NetworkAnalyst,</t> DIABLO, or both methods) are shown.
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Image Search Results


COVID-19 vaccine efficacy and safety – comparative studies.

Journal: Transplantation Reviews (Orlando, Fla.)

Article Title: COVID-19 and solid organ transplantation: Finding the right balance

doi: 10.1016/j.trre.2022.100710

Figure Lengend Snippet: COVID-19 vaccine efficacy and safety – comparative studies.

Article Snippet: Sattler et al. [ ] , Prospective single-centre cohort study , 104 patients: 39 KTR, 26 DP, 39 matched healthy controls , 100% BNT126b2 , Humoral response: Anti-S1 IgG: Euroimmun ELISA Anti-S1 IgA: Euroimmun ELISA Neutralizing Ab's: sVNT GenScript Cellular response : FACS , Anti-S1 IgG response: 2.6% KTR, 84.6% DP, 100% controls Anti-S1 IgA response: 10 .3% KTR, 84.6% DP, 97.4% controls Neutralizing Abs: 0% KTR, 76.9% DP, 100% controls , Spike specific CD4+ responders: 92.3% KTR, 100% DP, 100% controls Spike specific CD8+ responders : 5.1% KTR, 30.8% DP, 46.2% controls , Acute rejection: 0% , NR , Anti-S1 IgG response : 100% controls vs 2.6% KTR, p < 0.0001 Anti-S1 IgA response: 97.44% controls vs 10.26% KTR, p < 0.0001 Spike specific CD4+ responders : 100% controls vs 92.3% KTR, p = 0.240 Spike specific CD8+ responders: 46.15% controls vs 5.13% KTR, p < 0.0001.

Techniques: Enzyme-linked Immunosorbent Assay, Enzyme-linked Immunospot, Injection, Transplantation Assay

COVID-19 vaccine efficacy and safety – non-comparative studies.

Journal: Transplantation Reviews (Orlando, Fla.)

Article Title: COVID-19 and solid organ transplantation: Finding the right balance

doi: 10.1016/j.trre.2022.100710

Figure Lengend Snippet: COVID-19 vaccine efficacy and safety – non-comparative studies.

Article Snippet: Sattler et al. [ ] , Prospective single-centre cohort study , 104 patients: 39 KTR, 26 DP, 39 matched healthy controls , 100% BNT126b2 , Humoral response: Anti-S1 IgG: Euroimmun ELISA Anti-S1 IgA: Euroimmun ELISA Neutralizing Ab's: sVNT GenScript Cellular response : FACS , Anti-S1 IgG response: 2.6% KTR, 84.6% DP, 100% controls Anti-S1 IgA response: 10 .3% KTR, 84.6% DP, 97.4% controls Neutralizing Abs: 0% KTR, 76.9% DP, 100% controls , Spike specific CD4+ responders: 92.3% KTR, 100% DP, 100% controls Spike specific CD8+ responders : 5.1% KTR, 30.8% DP, 46.2% controls , Acute rejection: 0% , NR , Anti-S1 IgG response : 100% controls vs 2.6% KTR, p < 0.0001 Anti-S1 IgA response: 97.44% controls vs 10.26% KTR, p < 0.0001 Spike specific CD4+ responders : 100% controls vs 92.3% KTR, p = 0.240 Spike specific CD8+ responders: 46.15% controls vs 5.13% KTR, p < 0.0001.

Techniques: Luminex, Enzyme-linked Immunospot, Injection, Flow Cytometry, Transplantation Assay, Enzyme-linked Immunosorbent Assay

COVID-19 vaccine efficacy and safety – comparative studies.

Journal: Transplantation Reviews (Orlando, Fla.)

Article Title: COVID-19 and solid organ transplantation: Finding the right balance

doi: 10.1016/j.trre.2022.100710

Figure Lengend Snippet: COVID-19 vaccine efficacy and safety – comparative studies.

Article Snippet: Sattler et al. [ ] , Prospective single-centre cohort study , 104 patients: 39 KTR, 26 DP, 39 matched healthy controls , 100% BNT126b2 , Humoral response: Anti-S1 IgG: Euroimmun ELISA Anti-S1 IgA: Euroimmun ELISA Neutralizing Ab's: sVNT GenScript Cellular response : FACS , Anti-S1 IgG response: 2.6% KTR, 84.6% DP, 100% controls Anti-S1 IgA response: 10 .3% KTR, 84.6% DP, 97.4% controls Neutralizing Abs: 0% KTR, 76.9% DP, 100% controls , Spike specific CD4+ responders: 92.3% KTR, 100% DP, 100% controls Spike specific CD8+ responders : 5.1% KTR, 30.8% DP, 46.2% controls , Acute rejection: 0% , NR , Anti-S1 IgG response : 100% controls vs 2.6% KTR, p < 0.0001 Anti-S1 IgA response: 97.44% controls vs 10.26% KTR, p < 0.0001 Spike specific CD4+ responders : 100% controls vs 92.3% KTR, p = 0.240 Spike specific CD8+ responders: 46.15% controls vs 5.13% KTR, p < 0.0001.

Techniques: Enzyme-linked Immunosorbent Assay, Enzyme-linked Immunospot, Injection, Transplantation Assay

Multi-omics integration to reduce overfitting by identifying of more biologically relevant features. (A) Comparison of the performance of a multi-omics model (DIABLO) to that of single-omics models of equivalent complexities, fit separately to individual omics datasets (otu, operating taxonomic units of the fecal microbiome; cpg, blood-based DNA methylation; protein, plasma proteomics; flow, cell counts by flow cytometry; mrna, whole blood transcriptomics). Mean squared error (MSE) differed significantly across all models (Kruskal-Wallis test; p = 0.0023), with the multi-omics model achieving significantly lower error (and better performance) when compared to all other models, with the exception of the proteomics-derived model (p = 0.068). (B) Integrated minimally-connected first-order network of features identified by DIABLO from transcriptomic, proteomics, and epigenetic data. Novel nodes identified from integration are highlighted in orange. (C) Selected enriched pathways and (D–F) selected enriched genes (mRNA) or proteins (proteomics) identified from integration (NetworkAnalyst, DIABLO, or both methods) are shown.

Journal: Frontiers in Immunology

Article Title: Multi-Omic Data Integration Allows Baseline Immune Signatures to Predict Hepatitis B Vaccine Response in a Small Cohort

doi: 10.3389/fimmu.2020.578801

Figure Lengend Snippet: Multi-omics integration to reduce overfitting by identifying of more biologically relevant features. (A) Comparison of the performance of a multi-omics model (DIABLO) to that of single-omics models of equivalent complexities, fit separately to individual omics datasets (otu, operating taxonomic units of the fecal microbiome; cpg, blood-based DNA methylation; protein, plasma proteomics; flow, cell counts by flow cytometry; mrna, whole blood transcriptomics). Mean squared error (MSE) differed significantly across all models (Kruskal-Wallis test; p = 0.0023), with the multi-omics model achieving significantly lower error (and better performance) when compared to all other models, with the exception of the proteomics-derived model (p = 0.068). (B) Integrated minimally-connected first-order network of features identified by DIABLO from transcriptomic, proteomics, and epigenetic data. Novel nodes identified from integration are highlighted in orange. (C) Selected enriched pathways and (D–F) selected enriched genes (mRNA) or proteins (proteomics) identified from integration (NetworkAnalyst, DIABLO, or both methods) are shown.

Article Snippet: To determine the molecular and immunological differences that might influence vaccine responses, an integrative analysis was performed using either two or three omics datasets (transcriptomics, proteomics and/or epigenomics) comparing responders vs. non-responders using NetworkAnalyst ( ).

Techniques: Biomarker Discovery, Comparison, DNA Methylation Assay, Clinical Proteomics, Flow Cytometry, Derivative Assay