Review





Similar Products

anti cd11c pe vio770  (Miltenyi Biotec)
96
Miltenyi Biotec anti cd11c pe vio770
Anti Cd11c Pe Vio770, supplied by Miltenyi Biotec, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/anti cd11c pe vio770/product/Miltenyi Biotec
Average 96 stars, based on 1 article reviews
anti cd11c pe vio770 - by Bioz Stars, 2026-04
96/100 stars
  Buy from Supplier
anti cd11c  (Cytek Biosciences)
90
Cytek Biosciences anti cd11c
Anti Cd11c, supplied by Cytek Biosciences, 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/result/anti cd11c/product/Cytek Biosciences
Average 90 stars, based on 1 article reviews
anti cd11c - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
anti cd11c rabbit mab  (Cell Signaling Technology Inc)
96
Cell Signaling Technology Inc anti cd11c rabbit mab
Anti Cd11c Rabbit Mab, supplied by Cell Signaling Technology Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/anti cd11c rabbit mab/product/Cell Signaling Technology Inc
Average 96 stars, based on 1 article reviews
anti cd11c rabbit mab - by Bioz Stars, 2026-04
96/100 stars
  Buy from Supplier
cd11c  (Miltenyi Biotec)
94
Miltenyi Biotec cd11c
Cd11c, supplied by Miltenyi Biotec, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/cd11c/product/Miltenyi Biotec
Average 94 stars, based on 1 article reviews
cd11c - by Bioz Stars, 2026-04
94/100 stars
  Buy from Supplier
violetflour 450 anti human cd11c  (Cytek Biosciences)
90
Cytek Biosciences violetflour 450 anti human cd11c
Machine learning classification identifies predictive immune markers distinguishing PDAC from healthy individuals (A) Schematic overview of the machine learning workflow used to identify predictive immune markers from high-dimensional spectral flow cytometry data. Peripheral blood samples from 38 healthy individuals and 39 treatment-naive PDAC patients (stages I–IV) were analyzed. Mean fluorescence intensity (MFI) values of surface markers were extracted from CD45 + cells, asinh-normalized, and aggregated at the individual level. The dataset was split into 70% training and 30% testing sets for model development and evaluation. (B–D) Top 10 predictive markers ranked by feature importance scores across three independent classifiers: random forest (RF; B), gradient boosting (GB; C), and Bayesian additive regression tree (BART; D). Markers are ranked by their Gini importance scores. (E and F) Receiver operating characteristic (ROC) curves showing model performance in classifying healthy versus PDAC samples (E) and in predicting PDAC stages (F). Curves represent random forest (orange), GB (green), and BART (blue) classifiers, with area under the curve (AUC) values indicating model accuracy. (G and H) Overall expression levels of CD95 and CD45RA, and (H) the representative expression patterns of CD95 in PD1+ CD4 + T cells, CD45RA+ terminal effector (TE) CD8 + T cell, <t>CD11c+</t> dendritic cells (DCs), and non-classical monocytes (Mo) of healthy individuals and PDAC patients. Each bar represents Z score normalized mean fluorescence intensity (MFI) across individuals. The data are represented as mean ± SEM, and the t test p value, indicating statistical significance, is displayed at the top of each violin and boxplot. ∗∗∗ p < 0.001; NS, non-significance.
Violetflour 450 Anti Human Cd11c, supplied by Cytek Biosciences, 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/result/violetflour 450 anti human cd11c/product/Cytek Biosciences
Average 90 stars, based on 1 article reviews
violetflour 450 anti human cd11c - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
rabbit monoclonal anti cd11c antibody  (Cell Signaling Technology Inc)
96
Cell Signaling Technology Inc rabbit monoclonal anti cd11c antibody
Machine learning classification identifies predictive immune markers distinguishing PDAC from healthy individuals (A) Schematic overview of the machine learning workflow used to identify predictive immune markers from high-dimensional spectral flow cytometry data. Peripheral blood samples from 38 healthy individuals and 39 treatment-naive PDAC patients (stages I–IV) were analyzed. Mean fluorescence intensity (MFI) values of surface markers were extracted from CD45 + cells, asinh-normalized, and aggregated at the individual level. The dataset was split into 70% training and 30% testing sets for model development and evaluation. (B–D) Top 10 predictive markers ranked by feature importance scores across three independent classifiers: random forest (RF; B), gradient boosting (GB; C), and Bayesian additive regression tree (BART; D). Markers are ranked by their Gini importance scores. (E and F) Receiver operating characteristic (ROC) curves showing model performance in classifying healthy versus PDAC samples (E) and in predicting PDAC stages (F). Curves represent random forest (orange), GB (green), and BART (blue) classifiers, with area under the curve (AUC) values indicating model accuracy. (G and H) Overall expression levels of CD95 and CD45RA, and (H) the representative expression patterns of CD95 in PD1+ CD4 + T cells, CD45RA+ terminal effector (TE) CD8 + T cell, <t>CD11c+</t> dendritic cells (DCs), and non-classical monocytes (Mo) of healthy individuals and PDAC patients. Each bar represents Z score normalized mean fluorescence intensity (MFI) across individuals. The data are represented as mean ± SEM, and the t test p value, indicating statistical significance, is displayed at the top of each violin and boxplot. ∗∗∗ p < 0.001; NS, non-significance.
Rabbit Monoclonal Anti Cd11c Antibody, supplied by Cell Signaling Technology Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/rabbit monoclonal anti cd11c antibody/product/Cell Signaling Technology Inc
Average 96 stars, based on 1 article reviews
rabbit monoclonal anti cd11c antibody - by Bioz Stars, 2026-04
96/100 stars
  Buy from Supplier
cd11c magnetic beads  (Miltenyi Biotec)
96
Miltenyi Biotec cd11c magnetic beads
Machine learning classification identifies predictive immune markers distinguishing PDAC from healthy individuals (A) Schematic overview of the machine learning workflow used to identify predictive immune markers from high-dimensional spectral flow cytometry data. Peripheral blood samples from 38 healthy individuals and 39 treatment-naive PDAC patients (stages I–IV) were analyzed. Mean fluorescence intensity (MFI) values of surface markers were extracted from CD45 + cells, asinh-normalized, and aggregated at the individual level. The dataset was split into 70% training and 30% testing sets for model development and evaluation. (B–D) Top 10 predictive markers ranked by feature importance scores across three independent classifiers: random forest (RF; B), gradient boosting (GB; C), and Bayesian additive regression tree (BART; D). Markers are ranked by their Gini importance scores. (E and F) Receiver operating characteristic (ROC) curves showing model performance in classifying healthy versus PDAC samples (E) and in predicting PDAC stages (F). Curves represent random forest (orange), GB (green), and BART (blue) classifiers, with area under the curve (AUC) values indicating model accuracy. (G and H) Overall expression levels of CD95 and CD45RA, and (H) the representative expression patterns of CD95 in PD1+ CD4 + T cells, CD45RA+ terminal effector (TE) CD8 + T cell, <t>CD11c+</t> dendritic cells (DCs), and non-classical monocytes (Mo) of healthy individuals and PDAC patients. Each bar represents Z score normalized mean fluorescence intensity (MFI) across individuals. The data are represented as mean ± SEM, and the t test p value, indicating statistical significance, is displayed at the top of each violin and boxplot. ∗∗∗ p < 0.001; NS, non-significance.
Cd11c Magnetic Beads, supplied by Miltenyi Biotec, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/cd11c magnetic beads/product/Miltenyi Biotec
Average 96 stars, based on 1 article reviews
cd11c magnetic beads - by Bioz Stars, 2026-04
96/100 stars
  Buy from Supplier
fitc anti mouse cd11c antibody  (Elabscience Biotechnology)
94
Elabscience Biotechnology fitc anti mouse cd11c antibody
Machine learning classification identifies predictive immune markers distinguishing PDAC from healthy individuals (A) Schematic overview of the machine learning workflow used to identify predictive immune markers from high-dimensional spectral flow cytometry data. Peripheral blood samples from 38 healthy individuals and 39 treatment-naive PDAC patients (stages I–IV) were analyzed. Mean fluorescence intensity (MFI) values of surface markers were extracted from CD45 + cells, asinh-normalized, and aggregated at the individual level. The dataset was split into 70% training and 30% testing sets for model development and evaluation. (B–D) Top 10 predictive markers ranked by feature importance scores across three independent classifiers: random forest (RF; B), gradient boosting (GB; C), and Bayesian additive regression tree (BART; D). Markers are ranked by their Gini importance scores. (E and F) Receiver operating characteristic (ROC) curves showing model performance in classifying healthy versus PDAC samples (E) and in predicting PDAC stages (F). Curves represent random forest (orange), GB (green), and BART (blue) classifiers, with area under the curve (AUC) values indicating model accuracy. (G and H) Overall expression levels of CD95 and CD45RA, and (H) the representative expression patterns of CD95 in PD1+ CD4 + T cells, CD45RA+ terminal effector (TE) CD8 + T cell, <t>CD11c+</t> dendritic cells (DCs), and non-classical monocytes (Mo) of healthy individuals and PDAC patients. Each bar represents Z score normalized mean fluorescence intensity (MFI) across individuals. The data are represented as mean ± SEM, and the t test p value, indicating statistical significance, is displayed at the top of each violin and boxplot. ∗∗∗ p < 0.001; NS, non-significance.
Fitc Anti Mouse Cd11c Antibody, supplied by Elabscience Biotechnology, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/fitc anti mouse cd11c antibody/product/Elabscience Biotechnology
Average 94 stars, based on 1 article reviews
fitc anti mouse cd11c antibody - by Bioz Stars, 2026-04
94/100 stars
  Buy from Supplier

Image Search Results


Machine learning classification identifies predictive immune markers distinguishing PDAC from healthy individuals (A) Schematic overview of the machine learning workflow used to identify predictive immune markers from high-dimensional spectral flow cytometry data. Peripheral blood samples from 38 healthy individuals and 39 treatment-naive PDAC patients (stages I–IV) were analyzed. Mean fluorescence intensity (MFI) values of surface markers were extracted from CD45 + cells, asinh-normalized, and aggregated at the individual level. The dataset was split into 70% training and 30% testing sets for model development and evaluation. (B–D) Top 10 predictive markers ranked by feature importance scores across three independent classifiers: random forest (RF; B), gradient boosting (GB; C), and Bayesian additive regression tree (BART; D). Markers are ranked by their Gini importance scores. (E and F) Receiver operating characteristic (ROC) curves showing model performance in classifying healthy versus PDAC samples (E) and in predicting PDAC stages (F). Curves represent random forest (orange), GB (green), and BART (blue) classifiers, with area under the curve (AUC) values indicating model accuracy. (G and H) Overall expression levels of CD95 and CD45RA, and (H) the representative expression patterns of CD95 in PD1+ CD4 + T cells, CD45RA+ terminal effector (TE) CD8 + T cell, CD11c+ dendritic cells (DCs), and non-classical monocytes (Mo) of healthy individuals and PDAC patients. Each bar represents Z score normalized mean fluorescence intensity (MFI) across individuals. The data are represented as mean ± SEM, and the t test p value, indicating statistical significance, is displayed at the top of each violin and boxplot. ∗∗∗ p < 0.001; NS, non-significance.

Journal: iScience

Article Title: Peripheral immune landscape in pancreatic ductal adenocarcinoma reveals expansion of effector states with disease progression

doi: 10.1016/j.isci.2026.115034

Figure Lengend Snippet: Machine learning classification identifies predictive immune markers distinguishing PDAC from healthy individuals (A) Schematic overview of the machine learning workflow used to identify predictive immune markers from high-dimensional spectral flow cytometry data. Peripheral blood samples from 38 healthy individuals and 39 treatment-naive PDAC patients (stages I–IV) were analyzed. Mean fluorescence intensity (MFI) values of surface markers were extracted from CD45 + cells, asinh-normalized, and aggregated at the individual level. The dataset was split into 70% training and 30% testing sets for model development and evaluation. (B–D) Top 10 predictive markers ranked by feature importance scores across three independent classifiers: random forest (RF; B), gradient boosting (GB; C), and Bayesian additive regression tree (BART; D). Markers are ranked by their Gini importance scores. (E and F) Receiver operating characteristic (ROC) curves showing model performance in classifying healthy versus PDAC samples (E) and in predicting PDAC stages (F). Curves represent random forest (orange), GB (green), and BART (blue) classifiers, with area under the curve (AUC) values indicating model accuracy. (G and H) Overall expression levels of CD95 and CD45RA, and (H) the representative expression patterns of CD95 in PD1+ CD4 + T cells, CD45RA+ terminal effector (TE) CD8 + T cell, CD11c+ dendritic cells (DCs), and non-classical monocytes (Mo) of healthy individuals and PDAC patients. Each bar represents Z score normalized mean fluorescence intensity (MFI) across individuals. The data are represented as mean ± SEM, and the t test p value, indicating statistical significance, is displayed at the top of each violin and boxplot. ∗∗∗ p < 0.001; NS, non-significance.

Article Snippet: violetFlour 450 Anti-Human CD11c (Clone: 3.9) , Cytek Biosciences , Cat# 75–0116_T100; PRID: AB_2621938.

Techniques: Flow Cytometry, Fluorescence, Biomarker Discovery, Expressing