Table 2_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xls by Haoxue Zhang (12208580)

“…The optimal model, based on seven histone-related genes, showed the highest C-index and was validated in both training and validation cohorts. …”

Table 3_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xlsx by Haoxue Zhang (12208580)

“…The optimal model, based on seven histone-related genes, showed the highest C-index and was validated in both training and validation cohorts. …”

Table 1_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.xlsx by Haoxue Zhang (12208580)

“…The optimal model, based on seven histone-related genes, showed the highest C-index and was validated in both training and validation cohorts. …”

Data_Sheet_1_Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield... by Uttam Khatri (12689072)

“…Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. …”

Data Sheet 2_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.zip by Haoxue Zhang (12208580)

“…The optimal model, based on seven histone-related genes, showed the highest C-index and was validated in both training and validation cohorts. …”

Data Sheet 1_Histone-related gene WDR77 promotes tumor progression through cell cycle regulation in skin cutaneous melanoma.zip by Haoxue Zhang (12208580)

“…The optimal model, based on seven histone-related genes, showed the highest C-index and was validated in both training and validation cohorts. …”

Table 2_Cellular hierarchy framework based on single-cell and bulk RNA sequencing reveals fatty acid metabolic biomarker MYDGF as a therapeutic target for ccRCC.xlsx by Ning Wang (108353)

Table 1_Cellular hierarchy framework based on single-cell and bulk RNA sequencing reveals fatty acid metabolic biomarker MYDGF as a therapeutic target for ccRCC.docx by Ning Wang (108353)

Data Sheet 1_Countrywide Corchorus olitorius L. core collection shows an adaptive potential for future climate in Benin.xlsx by Dèdéou A. Tchokponhoué (17403983)

“…The spatial variation of the genomic diversity painted an increasing trend following the South-North ecological gradient, giving rise to four optimal genetic groups based on STRUCTURE analysis while the neighbour-joining analysis revealed three clusters. …”

Data Sheet 2_Countrywide Corchorus olitorius L. core collection shows an adaptive potential for future climate in Benin.docx by Dèdéou A. Tchokponhoué (17403983)

“…The spatial variation of the genomic diversity painted an increasing trend following the South-North ecological gradient, giving rise to four optimal genetic groups based on STRUCTURE analysis while the neighbour-joining analysis revealed three clusters. …”

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). …”

PathOlOgics_RBCs Python Scripts.zip by Ahmed Elsafty (16943883)

“…</p><p dir="ltr">In terms of classification, a second algorithm was developed and employed to preliminary sort or group the individual cells (after excluding the overlapping cells manually) into different categories using five geometric measurements applied to the extracted contour from each binary image mask (see PathOlOgics_script_2; preliminary shape measurements). …”

Code by Baoqiang Chen (21099509)

“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”

Core data by Baoqiang Chen (21099509)

“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”