Table 5_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Data Sheet 1_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.docx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Table 7_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Table 4_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Table 1_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Table 3_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Table 2_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.xlsx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Table 6_Integrated analysis of stem cell-related genes shared between type 2 diabetes mellitus and sepsis.docx by Yubo Wang (556762)

“…The stem-cell-related biomarkers were discovered through combining functional similarity analysis, machine learning algorithms, and receiver operating characteristic (ROC) curves. …”

Image 1_Immune-molecular nexus in reproductive disorders: mechanisms linking POI and RSA.pdf by Chen Chen (6544)

“…The comprehensive evaluation includes functional annotation, protein-protein interaction (PPI) network, transcription factor (TF) gene regulatory network, microRNA (miRNA) gene regulatory network. …”

Table 1_ENO1-related gene signature predicts prognosis and therapeutic response in diffuse large B-cell lymphoma.docx by Wenli Yan (9235217)

“…Here, we integrated multi-omics profiling (RIP-seq, RNA-seq, and protein interactome analysis) to identify ERGs and established a prognostic model by machine learning algorithms.…”

Data Sheet 4_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…Machine learning algorithms identified COASY, FTSJ1, and MOGS as pivotal genes. …”

Data Sheet 5_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.zip by Binyu Wang (7375019)

“…Machine learning algorithms identified COASY, FTSJ1, and MOGS as pivotal genes. …”

Data Sheet 1_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.zip by Binyu Wang (7375019)

“…Machine learning algorithms identified COASY, FTSJ1, and MOGS as pivotal genes. …”

Data Sheet 2_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…Machine learning algorithms identified COASY, FTSJ1, and MOGS as pivotal genes. …”

Data Sheet 3_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…Machine learning algorithms identified COASY, FTSJ1, and MOGS as pivotal genes. …”

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)

“…A protein–protein interaction (PPI) network was constructed, and Mfuzz clustering analyzed temporal expression patterns. …”

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)

“…A protein–protein interaction (PPI) network was constructed, and Mfuzz clustering analyzed temporal expression patterns. …”

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)

“…A protein–protein interaction (PPI) network was constructed, and Mfuzz clustering analyzed temporal expression patterns. …”

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)

“…A protein–protein interaction (PPI) network was constructed, and Mfuzz clustering analyzed temporal expression patterns. …”

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)

“…A protein–protein interaction (PPI) network was constructed, and Mfuzz clustering analyzed temporal expression patterns. …”