Details of 23 basic benchmark functions. by Ying Li (38224)

Linear relationship between daily COVID-19 case importations and the relative change in community transmission over the course of one-month by effective reproductive number (Rt) wi... by Joshua M. Chevalier (12551331)

Optimal number of reference genes for various conditions. by Lisa Stephan (6421325)

Optimal stability ranking of candidate reference genes using geNorm, NormFinder, BestKeeper, and rank aggregation method. by Marina Linardić (10720448)

DataSheet_1_Patient-Derived Nasopharyngeal Cancer Organoids for Disease Modeling and Radiation Dose Optimization.docx by Sasidharan Swarnalatha Lucky (1683322)

“…This study is the first direct experimental evidence to predict optimal RT boost dose required to cause sufficient damage to recurrent hypoxic NPC tumor cells, which can be further used to develop dose-painting algorithms in clinical practice.…”

DataSheet_2_Patient-Derived Nasopharyngeal Cancer Organoids for Disease Modeling and Radiation Dose Optimization.docx by Sasidharan Swarnalatha Lucky (1683322)

“…This study is the first direct experimental evidence to predict optimal RT boost dose required to cause sufficient damage to recurrent hypoxic NPC tumor cells, which can be further used to develop dose-painting algorithms in clinical practice.…”

Table 1_Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma.docx by Bingxin Zhang (13011397)

Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Table 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Image 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …”

Data Sheet 1_Stemness-related gene signatures as a predictive tool for breast cancer radiosensitivity.pdf by Jinzhi Lai (6856568)

“…Radioresistant BRCA cells were examined using a colony formation assay. Key genes identified in the radiosensitivity signature were validated in vitro by qRT-PCR.…”

Data Sheet 1_Discovery of a DNA repair-associated radiosensitivity index for predicting radiotherapy efficacy in breast cancer.docx by Jianguang Lin (13032527)

“…The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and pRRophetic platform were used to predict treatment responses. …”

DataSheet_1_Computational identification and clinical validation of a novel risk signature based on coagulation-related lncRNAs for predicting prognosis, immunotherapy response, an... by Fang Zhang (197215)

“…</p>Methods<p>CRC samples from The Cancer Genome Atlas (TCGA) were used as the training set, while the substantial bulk or single-cell RNA transcriptomics from Gene Expression Omnibus (GEO) datasets and real-time quantitative PCR (RT-qPCR) data from CRC cell lines and paired frozen tissues were used for validation. …”

Image_2_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.tif by Cong Liu (66219)

“…By immunohistochemistry (IHC), Western blot, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiments, the expression levels of CHKB and PEMT in human, mouse, and cell lines were detected.…”

DataSheet_2_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.csv by Cong Liu (66219)

“…By immunohistochemistry (IHC), Western blot, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiments, the expression levels of CHKB and PEMT in human, mouse, and cell lines were detected.…”

Image_1_Construction of a novel choline metabolism-related signature to predict prognosis, immune landscape, and chemotherapy response in colon adenocarcinoma.tiff by Cong Liu (66219)

“…By immunohistochemistry (IHC), Western blot, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) experiments, the expression levels of CHKB and PEMT in human, mouse, and cell lines were detected.…”