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dad1  (Proteintech)


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    Proteintech dad1
    Dad1, supplied by Proteintech, used in various techniques. Bioz Stars score: 93/100, based on 3 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/dad1/product/Proteintech
    Average 93 stars, based on 3 article reviews
    dad1 - by Bioz Stars, 2026-04
    93/100 stars

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    Machine learning algorithms and hub targets acquisition. ( A ) The identification of potential targets using the randomForest algorithm. The abscissa of the figure is the score of importance, and the ordinate is the gene name. The red line represents the cut-off value. ( B ) Univariate Cox regression analysis of DP subtype signature genes in TCGA BLCA cohort. ( C ) A LASSO model was employed to identify significant genes. The partial likelihood deviance was plotted as a function of log(λ). Vertical lines denote the 1-SE rule for model selection. 27 genes were selected with non-zero coefficients at the optimal λ. ( D ) Venn diagram for identifying hub genes of BLCA. ( E ) Single gene GSEA enrichment analysis for <t>DAD1</t> based on hallmark gene set. ( F ) Single gene GSEA enrichment analysis for CYP1B1 based on hallmark gene set. ( G ) Single gene GSEA enrichment analysis for REXO2 based on hallmark gene set. ( H ) Correlation between DAD1 and 22 infiltrating immune cells. ( I ) Correlation between CYP1B1 and 22 infiltrating immune cells. ( J ) Correlation between REXO2 and 22 infiltrating immune cells
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    Machine learning algorithms and hub targets acquisition. ( A ) The identification of potential targets using the randomForest algorithm. The abscissa of the figure is the score of importance, and the ordinate is the gene name. The red line represents the cut-off value. ( B ) Univariate Cox regression analysis of DP subtype signature genes in TCGA BLCA cohort. ( C ) A LASSO model was employed to identify significant genes. The partial likelihood deviance was plotted as a function of log(λ). Vertical lines denote the 1-SE rule for model selection. 27 genes were selected with non-zero coefficients at the optimal λ. ( D ) Venn diagram for identifying hub genes of BLCA. ( E ) Single gene GSEA enrichment analysis for <t>DAD1</t> based on hallmark gene set. ( F ) Single gene GSEA enrichment analysis for CYP1B1 based on hallmark gene set. ( G ) Single gene GSEA enrichment analysis for REXO2 based on hallmark gene set. ( H ) Correlation between DAD1 and 22 infiltrating immune cells. ( I ) Correlation between CYP1B1 and 22 infiltrating immune cells. ( J ) Correlation between REXO2 and 22 infiltrating immune cells
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    Machine learning algorithms and hub targets acquisition. ( A ) The identification of potential targets using the randomForest algorithm. The abscissa of the figure is the score of importance, and the ordinate is the gene name. The red line represents the cut-off value. ( B ) Univariate Cox regression analysis of DP subtype signature genes in TCGA BLCA cohort. ( C ) A LASSO model was employed to identify significant genes. The partial likelihood deviance was plotted as a function of log(λ). Vertical lines denote the 1-SE rule for model selection. 27 genes were selected with non-zero coefficients at the optimal λ. ( D ) Venn diagram for identifying hub genes of BLCA. ( E ) Single gene GSEA enrichment analysis for <t>DAD1</t> based on hallmark gene set. ( F ) Single gene GSEA enrichment analysis for CYP1B1 based on hallmark gene set. ( G ) Single gene GSEA enrichment analysis for REXO2 based on hallmark gene set. ( H ) Correlation between DAD1 and 22 infiltrating immune cells. ( I ) Correlation between CYP1B1 and 22 infiltrating immune cells. ( J ) Correlation between REXO2 and 22 infiltrating immune cells
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    Image Search Results


    Machine learning algorithms and hub targets acquisition. ( A ) The identification of potential targets using the randomForest algorithm. The abscissa of the figure is the score of importance, and the ordinate is the gene name. The red line represents the cut-off value. ( B ) Univariate Cox regression analysis of DP subtype signature genes in TCGA BLCA cohort. ( C ) A LASSO model was employed to identify significant genes. The partial likelihood deviance was plotted as a function of log(λ). Vertical lines denote the 1-SE rule for model selection. 27 genes were selected with non-zero coefficients at the optimal λ. ( D ) Venn diagram for identifying hub genes of BLCA. ( E ) Single gene GSEA enrichment analysis for DAD1 based on hallmark gene set. ( F ) Single gene GSEA enrichment analysis for CYP1B1 based on hallmark gene set. ( G ) Single gene GSEA enrichment analysis for REXO2 based on hallmark gene set. ( H ) Correlation between DAD1 and 22 infiltrating immune cells. ( I ) Correlation between CYP1B1 and 22 infiltrating immune cells. ( J ) Correlation between REXO2 and 22 infiltrating immune cells

    Journal: Journal of Translational Medicine

    Article Title: Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification

    doi: 10.1186/s12967-025-06682-1

    Figure Lengend Snippet: Machine learning algorithms and hub targets acquisition. ( A ) The identification of potential targets using the randomForest algorithm. The abscissa of the figure is the score of importance, and the ordinate is the gene name. The red line represents the cut-off value. ( B ) Univariate Cox regression analysis of DP subtype signature genes in TCGA BLCA cohort. ( C ) A LASSO model was employed to identify significant genes. The partial likelihood deviance was plotted as a function of log(λ). Vertical lines denote the 1-SE rule for model selection. 27 genes were selected with non-zero coefficients at the optimal λ. ( D ) Venn diagram for identifying hub genes of BLCA. ( E ) Single gene GSEA enrichment analysis for DAD1 based on hallmark gene set. ( F ) Single gene GSEA enrichment analysis for CYP1B1 based on hallmark gene set. ( G ) Single gene GSEA enrichment analysis for REXO2 based on hallmark gene set. ( H ) Correlation between DAD1 and 22 infiltrating immune cells. ( I ) Correlation between CYP1B1 and 22 infiltrating immune cells. ( J ) Correlation between REXO2 and 22 infiltrating immune cells

    Article Snippet: The slides were blocked for one hour and then incubated overnight at 4 °C with primary antibodies against DAD1 (1:100; CSB-PA03124A0Rb, CUSABIO, https://www.cusabio.com/ , China) and CYP1B1 (1:100; R24037 , Zenbio, China).

    Techniques: Selection

    Malignancy phenotypes and experimental validation of hubgenes. ( A ) Kaplan-Meier of OS with log-rank test in TCGA BLCA cohort, respectively, for the grouping of the median expression of DAD1, CYP1B1, and REXO2. ( B ) The relative expression of different stages for DAD1, CYP1B1, and REXO2, respectively. ( C ) The relative expression of different grades for DAD1, CYP1B1, and REXO2, respectively. Ns represents P-value > 0.05; *, **, ***, **** represents P-value < 0.05. ( D ) Western blotting of DAD1, CYP1B1, and REXO2 proteins in different tumor stages. β-actin was used as the reference protein. ( E ) The bar graphs show the relative expression of DAD1, CYP1B1, and REXO2 proteins in different tumor stages. ( F ) Immunohistochemistry of DAD1 and CYP1B1 proteins in four tumor stages

    Journal: Journal of Translational Medicine

    Article Title: Pathway-based cancer transcriptome deciphers a high-resolution intrinsic heterogeneity within bladder cancer classification

    doi: 10.1186/s12967-025-06682-1

    Figure Lengend Snippet: Malignancy phenotypes and experimental validation of hubgenes. ( A ) Kaplan-Meier of OS with log-rank test in TCGA BLCA cohort, respectively, for the grouping of the median expression of DAD1, CYP1B1, and REXO2. ( B ) The relative expression of different stages for DAD1, CYP1B1, and REXO2, respectively. ( C ) The relative expression of different grades for DAD1, CYP1B1, and REXO2, respectively. Ns represents P-value > 0.05; *, **, ***, **** represents P-value < 0.05. ( D ) Western blotting of DAD1, CYP1B1, and REXO2 proteins in different tumor stages. β-actin was used as the reference protein. ( E ) The bar graphs show the relative expression of DAD1, CYP1B1, and REXO2 proteins in different tumor stages. ( F ) Immunohistochemistry of DAD1 and CYP1B1 proteins in four tumor stages

    Article Snippet: The slides were blocked for one hour and then incubated overnight at 4 °C with primary antibodies against DAD1 (1:100; CSB-PA03124A0Rb, CUSABIO, https://www.cusabio.com/ , China) and CYP1B1 (1:100; R24037 , Zenbio, China).

    Techniques: Biomarker Discovery, Expressing, Western Blot, Immunohistochemistry