Table 2_A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery.xlsx by Su-Zhen Yang (21448877)

“…Eleven key features were identified through group comparisons and used for model development. Twenty machine learning algorithms were evaluated, and the top five algorithms were used to build the final model by stacking.…”

Supplementary Material for: Assessing the accuracy of the international evidence-based Kyoto guidelines for detecting malignancy in intraductal papillary mucinous neoplasms of the... by figshare admin karger (2628495)

“…Introduction Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic tumours with an associated risk of malignant transformation. Due to the widespread use of imaging techniques, the diagnosis of IPMNs has been rising. …”

Table 1_Explore potential immune-related targets of leeches in the treatment of type 2 diabetes based on network pharmacology and machine learning.xlsx by Tairan Hu (21086501)

“…Finally, we employed LASSO regression, SVM-RFE, XGBoost, and random forest algorithms to further predict potential targets, followed by validation through molecular docking.…”

Image 2_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Table 1_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.docx by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation by Zhen Chen (129176)

“…PANoptosis scores and immune cell infiltration were calculated by ssGSEA. Machine learning algorithms was used to select feature genes. ROC analysis evaluated their diagnostic performance. …”

Table 3_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.xlsx by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Table 4_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.docx by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Image 3_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Table 2_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.docx by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Image 1_Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.tif by Ignacio Boira (21562568)

“…Introduction<p>Manual scoring of polysomnography data is a laborious and complex process. Automatic scoring by current computer algorithms shows high agreement with manual scoring. …”

Supplementary file 1_Plasma FGF2 and YAP1 as novel biomarkers for MCI in the elderly: analysis via bioinformatics and clinical study.docx by Yejing Zhao (22123588)

“…To address this gap, datasets GSE29378 and GSE12685 were selected to screen differentially expressed genes (DEGs), and hub genes were identified by different algorithms. A total of 350 DEGs were identified in bioinformatics data mining. …”

Table 1_Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.docx by Ignacio Boira (21562568)

“…Introduction<p>Manual scoring of polysomnography data is a laborious and complex process. Automatic scoring by current computer algorithms shows high agreement with manual scoring. …”

Image 1_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

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)

“…Functional enrichment algorithms, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore, along with hdWGCNA analysis, were used to identify hub genes influencing high FAM of ccRCC. …”

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)

“…Functional enrichment algorithms, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore, along with hdWGCNA analysis, were used to identify hub genes influencing high FAM of ccRCC. …”

The information of samples. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”

Hub genes in the cyan module. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”

Hub genes in the pink module. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”

Hub genes in the blue module. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”