Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 حسب 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). …"

DataSheet_3_Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation.xlsx حسب Yuzhi Wang (690944)

"…Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). …"

DataSheet_1_Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation.pdf حسب Yuzhi Wang (690944)

"…Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). …"

DataSheet_2_Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation.pdf حسب Yuzhi Wang (690944)

"…Harnessing the differentially expressed genes (DEGs) identified from these clusters, we formulated an optimal predictive model by amalgamating 10 distinct machine learning algorithms across 101 unique combinations to compute the disulfidptosis score (DS). …"

NanoDB: Research Activity Data Management System حسب Lorenci Gjurgjaj (19702207)

"…<p dir="ltr">NanoDB is a Python-based application developed to optimize the management of experimental data in research settings. …"

An overview of the AutoPrognosis framework. حسب Yuchao Qin (14419669)

S1 Fig - حسب Aniket Ravan (3174171)

Table_4_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"

Image_2_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"

Table_2_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"

Image_1_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"

Image_4_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"

Table_1_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"

Image_3_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.tiff حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"

Table_3_Computed Tomography Imaging-Based Radiogenomics Analysis Reveals Hypoxia Patterns and Immunological Characteristics in Ovarian Cancer.xlsx حسب Songwei Feng (11200268)

"…Finally, optimal radiogenomics biomarkers for predicting the risk status of patients were developed by machine learning algorithms.…"