Image_1_A cancer-associated fibroblast gene signature predicts prognosis and therapy response in patients with pancreatic cancer.pdf by Jinbao Zhang (169020)

“…Finally, oncoPredict algorithm was implemented to assess drug sensitivity prediction between high- and low-risk cohorts. …”

Image_2_A cancer-associated fibroblast gene signature predicts prognosis and therapy response in patients with pancreatic cancer.pdf by Jinbao Zhang (169020)

“…Finally, oncoPredict algorithm was implemented to assess drug sensitivity prediction between high- and low-risk cohorts. …”

Image2_A novel prognostic signature and immune microenvironment characteristics associated with disulfidptosis in papillary thyroid carcinoma based on single-cell RNA sequencing.TI... by Zhenyu Liao (3093213)

“…GO and KEGG analyses were used for functional analyses. CIBERSORT and ESTIMATE algorithm helped with the immune infiltration estimation. qRT‒PCR and flow cytometry was performed to validate the hub gene expression and immune infiltration in clinical samples.…”

Image2_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.TIF by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Image4_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.TIF by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Table2_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.DOCX by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Table7_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.DOCX by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Image_2_Tumor senescence leads to poor survival and therapeutic resistance in human breast cancer.tif by Jingtong Zhai (9401036)

“…A nomogram was constructed by multivariate Cox regression. We used Oncopredict algorithm and real-world data from clinical trials to explore the value of Sindex in predicting response to cancer therapy.…”

Image3_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.TIF by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Table6_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.DOCX by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Image1_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.TIF by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Table_3_Tumor senescence leads to poor survival and therapeutic resistance in human breast cancer.xlsx by Jingtong Zhai (9401036)

“…A nomogram was constructed by multivariate Cox regression. We used Oncopredict algorithm and real-world data from clinical trials to explore the value of Sindex in predicting response to cancer therapy.…”

Table8_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.DOCX by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Table5_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.DOCX by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Network Scaling Invariants Help to Elucidate Basic Topological Principles of Proteins by Arun Krishnan (7979)

“…The algorithm converges toward the maximization of the modularity of the given protein network and, in doing so, allows the representation of the residues of the protein in terms of their intramodule degree, <i>z</i>, and participation coefficient, <i>P</i>. …”

Image_1_Tumor senescence leads to poor survival and therapeutic resistance in human breast cancer.tif by Jingtong Zhai (9401036)

“…A nomogram was constructed by multivariate Cox regression. We used Oncopredict algorithm and real-world data from clinical trials to explore the value of Sindex in predicting response to cancer therapy.…”

Table4_Comprehensive multiomics analysis of cuproptosis-related gene characteristics in hepatocellular carcinoma.DOCX by Jie Fu (478672)

“…An unsupervised clustering method was used to identify distinct expression patterns in 370 HCC patients from the TCGA-HCC cohort. …”

Image_3_Tumor senescence leads to poor survival and therapeutic resistance in human breast cancer.tif by Jingtong Zhai (9401036)

“…A nomogram was constructed by multivariate Cox regression. We used Oncopredict algorithm and real-world data from clinical trials to explore the value of Sindex in predicting response to cancer therapy.…”

Table_5_Tumor senescence leads to poor survival and therapeutic resistance in human breast cancer.xlsx by Jingtong Zhai (9401036)

“…A nomogram was constructed by multivariate Cox regression. We used Oncopredict algorithm and real-world data from clinical trials to explore the value of Sindex in predicting response to cancer therapy.…”

Table_2_Tumor senescence leads to poor survival and therapeutic resistance in human breast cancer.xlsx by Jingtong Zhai (9401036)

“…A nomogram was constructed by multivariate Cox regression. We used Oncopredict algorithm and real-world data from clinical trials to explore the value of Sindex in predicting response to cancer therapy.…”