biomarkers measuring protein fragments Search Results


protein biomarkers measured using  (SomaLogic)
90
SomaLogic protein biomarkers measured using
Receiver operating characteristic curves for random forest classifier trained using clinical and imaging features plus protein <t>biomarkers.</t> Receiver operating characteristic curves for (A) performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 20 protein biomarkers identified in the univariate screen, applied to the testing subset of COPDGene. (B) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 11 protein biomarkers found in the multivariable confirmation step, applied to the testing subset of COPDGene. (C) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to the testing subset of COPDGene. (D) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to all available complete data in PLuSS. AUC, area under the curve; PLuSS, Pittsburgh Lung Screening Study.
Protein Biomarkers Measured Using, supplied by SomaLogic, 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/biomarkers+measuring+protein+fragments/protein+biomarkers+measured+using/pmc10941119-152-20-24
Average 90 stars, based on 1 article reviews
protein biomarkers measured using - by Bioz Stars, 2026-10
90/100 stars
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commercial measurement services for protein biomarkers  (Protypia LLC)
90
Protypia LLC commercial measurement services for protein biomarkers
Receiver operating characteristic curves for random forest classifier trained using clinical and imaging features plus protein <t>biomarkers.</t> Receiver operating characteristic curves for (A) performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 20 protein biomarkers identified in the univariate screen, applied to the testing subset of COPDGene. (B) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 11 protein biomarkers found in the multivariable confirmation step, applied to the testing subset of COPDGene. (C) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to the testing subset of COPDGene. (D) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to all available complete data in PLuSS. AUC, area under the curve; PLuSS, Pittsburgh Lung Screening Study.
Commercial Measurement Services For Protein Biomarkers, supplied by Protypia LLC, 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/biomarkers+measuring+protein+fragments/commercial+measurement+services+for+protein+biomarkers/pm36870295-141-15-8
Average 90 stars, based on 1 article reviews
commercial measurement services for protein biomarkers - by Bioz Stars, 2026-10
90/100 stars
  Buy from Supplier
biomarkers measuring protein fragments  (ImmunoScience)
90
ImmunoScience biomarkers measuring protein fragments
Receiver operating characteristic curves for random forest classifier trained using clinical and imaging features plus protein <t>biomarkers.</t> Receiver operating characteristic curves for (A) performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 20 protein biomarkers identified in the univariate screen, applied to the testing subset of COPDGene. (B) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 11 protein biomarkers found in the multivariable confirmation step, applied to the testing subset of COPDGene. (C) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to the testing subset of COPDGene. (D) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to all available complete data in PLuSS. AUC, area under the curve; PLuSS, Pittsburgh Lung Screening Study.
Biomarkers Measuring Protein Fragments, supplied by ImmunoScience, 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/biomarkers+measuring+protein+fragments/biomarkers+measuring+protein+fragments/pm39774197-31-0-8
Average 90 stars, based on 1 article reviews
biomarkers measuring protein fragments - by Bioz Stars, 2026-10
90/100 stars
  Buy from Supplier

Image Search Results


Receiver operating characteristic curves for random forest classifier trained using clinical and imaging features plus protein biomarkers. Receiver operating characteristic curves for (A) performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 20 protein biomarkers identified in the univariate screen, applied to the testing subset of COPDGene. (B) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 11 protein biomarkers found in the multivariable confirmation step, applied to the testing subset of COPDGene. (C) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to the testing subset of COPDGene. (D) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to all available complete data in PLuSS. AUC, area under the curve; PLuSS, Pittsburgh Lung Screening Study.

Journal: BMJ Open Respiratory Research

Article Title: Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study

doi: 10.1136/bmjresp-2023-002219

Figure Lengend Snippet: Receiver operating characteristic curves for random forest classifier trained using clinical and imaging features plus protein biomarkers. Receiver operating characteristic curves for (A) performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 20 protein biomarkers identified in the univariate screen, applied to the testing subset of COPDGene. (B) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the 11 protein biomarkers found in the multivariable confirmation step, applied to the testing subset of COPDGene. (C) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to the testing subset of COPDGene. (D) Performance of the random forest classifier trained using clinical/imaging features (age, gender, smoking status, pack-years and baseline interstitial features) plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS, applied to all available complete data in PLuSS. AUC, area under the curve; PLuSS, Pittsburgh Lung Screening Study.

Article Snippet: While they are both research cohorts, COPDGene involves visits at 5-year time points, imaging using standard dose CT scans and protein biomarkers measured using SOMAlogic.

Techniques: Imaging

Variable importance for incident interstitial features prediction models. (A) Variable importance for the random forest classifier trained using only clinical/imaging features. (B) Variable importance for the random forest classifier trained using clinical/imaging features plus the 20 protein biomarkers identified in the univariate screen. (C) Variable importance for the random forest classifier trained using clinical/imaging features plus the 11 protein biomarkers found in the multivariable confirmation step. (D) Variable importance for the random forest classifier trained using clinical/imaging features plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS. PLuSS, Pittsburgh Lung Screening Study.

Journal: BMJ Open Respiratory Research

Article Title: Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study

doi: 10.1136/bmjresp-2023-002219

Figure Lengend Snippet: Variable importance for incident interstitial features prediction models. (A) Variable importance for the random forest classifier trained using only clinical/imaging features. (B) Variable importance for the random forest classifier trained using clinical/imaging features plus the 20 protein biomarkers identified in the univariate screen. (C) Variable importance for the random forest classifier trained using clinical/imaging features plus the 11 protein biomarkers found in the multivariable confirmation step. (D) Variable importance for the random forest classifier trained using clinical/imaging features plus the four protein biomarkers found in the multivariable confirmation step in COPDGene that were available in PLuSS. PLuSS, Pittsburgh Lung Screening Study.

Article Snippet: While they are both research cohorts, COPDGene involves visits at 5-year time points, imaging using standard dose CT scans and protein biomarkers measured using SOMAlogic.

Techniques: Imaging