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.…”

Data Sheet 1_TGM2 regulated by transcription factor NR3C1 drives p38 MAPK-mediated tumor progression and immune evasion in lung squamous cell carcinoma.zip by Chunlong Lin (18931437)

“…Key genes were screened via random forest algorithm. Functional validation was performed in NCI-H520 and SK-MES-1 cell lines. …”

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). …”

DataSheet1_Identification of potential auxin response candidate genes for soybean rapid canopy coverage through comparative evolution and expression analysis.zip by Deisiany Ferreira Neres (10484791)

“…Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. …”

Supplementary file 1_Comprehensive analysis of phagocytosis regulatory genes in bladder cancer: implications for prognosis and immunotherapy.xlsx by Xueming Ma (2119150)

“…</p>Methods<p>Multi-omics data from the TCGA and GEO databases were integrated, and strict data preprocessing was carried out. A variety of algorithms and analysis techniques, such as Kaplan-Meier analysis, Cox regression analysis, and ConsensusClusterPlus clustering analysis, were used to identify PRGs related to the prognosis of bladder cancer patients, and functional analysis and clustering analysis were conducted in depth. …”

Supplementary file 1_Socioeconomic status and lifestyle as factors of multimorbidity among older adults in China: results from the China Health and Retirement Longitudinal Survey.d... by Wei Gong (112494)

“…</p>Results<p>XGBoost achieved the best predictive performance (AUC = 0.788 on the test set), outperforming both linear and non-linear models across most evaluation metrics. …”

Table 1_Identification of crosstalk genes and diagnostic biomarkers in systemic sclerosis associated sarcopenia through integrative analysis and machine learning.docx by Yanfang Wu (206076)

“…PCR validation confirmed the differential expression of NOX4 and NEK6 in both SSc and SSc-associated sarcopenia, demonstrating high predictive accuracy. …”

Table 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.xlsx by Ke Ma (260231)

“…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …”