Table 1_Construction and validation of immune prognosis model for lung adenocarcinoma based on machine learning.docx by Jinyu Zheng (734257)

“…Three machine learning algorithms—Random Forest, LASSO, and SVM-RFE—were applied to identify key hub genes. …”

Table 2_Comprehensive analysis of immunogenic cell death-related genes in liver ischemia-reperfusion injury.xlsx by Kai Lu (166354)

“…The RF and SVM machine learning algorithms were finally chosen to construct the models. …”

Supplementary file 1_Transcriptomic analysis and machine learning modeling identifies novel biomarkers and genetic characteristics of hypertrophic cardiomyopathy.xlsx by Feng Zhang (6548)

“…A predictive model for HCM was developed through systematic evaluation of 113 combinations of 12 machine-learning algorithms, employing 10-fold cross-validation on training datasets and external validation using an independent cohort (GSE180313).…”

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)

“…However, there is still a notable absence of novel biomarkers that are both efficient, minimally invasive, and cost-effective in real-world clinical settings. …”

Table 1_Comprehensive analysis of immunogenic cell death-related genes in liver ischemia-reperfusion injury.xlsx by Kai Lu (166354)

“…The RF and SVM machine learning algorithms were finally chosen to construct the models. …”

Image1_Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis.pdf by Lei Zhong (192135)

“…Subsequently, we used several machine learning algorithms to identify candidate biomarkers and construct a diagnostic nomogram for T2DM and AP. …”

Table1_Exploring the pathogenesis, biomarkers, and potential drugs for type 2 diabetes mellitus and acute pancreatitis through a comprehensive bioinformatic analysis.xlsx by Lei Zhong (192135)

“…Subsequently, we used several machine learning algorithms to identify candidate biomarkers and construct a diagnostic nomogram for T2DM and AP. …”

Supplementary file 1_Identification of glycolysis-related clusters and immune cell infiltration in hepatic fibrosis progression using machine learning models and experimental valid... by Guanglin Xiao (18113302)

“…Integrated weighted gene co-expression network analysis (WGCNA) with six machine learning algorithms to identify core GRGs genes associated with HF progression, and systematically characterized their biological functions and immunoregulatory roles through immune infiltration assessment, functional enrichment, consensus clustering, and single-cell differential state analysis. …”

Supplementary file 1_CYLD as a key regulator of myocardial infarction-to-heart failure transition revealed by multi-omics integration.docx by Jingya Xu (5572547)

“…Our multistep analytical pipeline included weighted gene coexpression network analysis (WGCNA) to map interacting genes, machine learning algorithms for robust classification, functional annotation via Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore biological pathways, CIBERSORT correlation analysis linking hub genes with immune cell states, transcriptional regulation profiling of key hubs, and single-cell sequencing to assess the functional relevance of these hubs.…”

Table 1_Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.docx by Chen Lin (95910)

“…Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI.…”

Table 3_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 5_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 8_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 6_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 4_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Image 5_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.jpeg by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 9_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Table 7_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Image 3_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.jpeg by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”

Image 4_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.jpeg by Shenglong Wang (569676)

“…It was identified as a key diagnostic gene by both machine learning algorithms, showing a high diagnostic accuracy (AUC = 0.974). …”