Table_4_A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.xls by Bo Cheng (1371330)

“…After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier.…”

Table1_Prognostic value of LECT2 and relevance to immune infiltration in hepatocellular carcinoma.DOCX by Jiangfeng Qin (5618006)

“…The GO/KEGG enrichment analysis of LECT2 co-expression and gene set enrichment analysis (GSEA) was performed using the R software package. …”

Data Sheet 1_Diagnostic lncRNA biomarkers and immune-related ceRNA networks for osteonecrosis of the femoral head in metabolic syndrome identified by plasma RNA sequencing and mach... by Haoyan Sun (22172911)

“…The MetS dataset from the Gene Expression Omnibus (GEO) was integrated, and weighted gene co-expression network analysis (WGCNA), functional enrichment, protein-protein interaction (PPI) network analysis, MCODE, CytoHubba-MCC, and random forest (RF) algorithms were employed to identify hub mRNAs and their associated lncRNAs. …”

Table 2_Identification and verification of biomarkers associated with neutrophils in acute myocardial infarction: integrated analysis of bulk RNA-seq, expression quantitative trait... by Guoqing Liu (93712)

“…Genes with correlation coefficients >0.7 with neutrophils were selected, and their representativeness was confirmed by functional enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was performed to screen for AMI-related modular genes. …”

Table_3_A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.xls by Bo Cheng (1371330)

“…After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier.…”

Table_2_A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.xls by Bo Cheng (1371330)

“…After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier.…”

DataSheet1_Identification of cuproptosis-related biomarkers and analysis of immune infiltration in allograft lung ischemia-reperfusion injury.docx by Jianying Qin (12042813)

“…We identified differentially expressed CRGs (DE-CRGs) and performed functional analyses. Biomarker genes were selected using three machine learning algorithms. …”

DataSheet_1_A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.csv by Bo Cheng (1371330)

“…After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier.…”

DataSheet_1_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.pdf by GuoHua You (17140984)

“…Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. …”

Table_3_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.xls by GuoHua You (17140984)

“…Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. …”

Table_2_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.xls by GuoHua You (17140984)

“…Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. …”

Table_1_Characterizing mitochondrial features in osteoarthritis through integrative multi-omics and machine learning analysis.docx by Yinteng Wu (14825398)

“…We utilized single-sample gene set enrichment analysis (ssGSEA), gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) analyses to explore the functional mechanisms associated with these genes. …”

Table_1_Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis.xls by GuoHua You (17140984)

“…Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. …”

Data_Sheet_1_Mortality Prediction in Sepsis With an Immune-Related Transcriptomics Signature: A Multi-Cohort Analysis.pdf by Louis Kreitmann (475618)

“…</p>Methods<p>Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms.…”

Table_1_Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.docx by Hao Yang (328526)

“…Machine learning algorithms and COX regression models were employed to further screen genes related to patient prognosis. …”

Image_1_Molecular mechanisms of pancreatic cancer liver metastasis: the role of PAK2.tif by Hao Yang (328526)

“…Machine learning algorithms and COX regression models were employed to further screen genes related to patient prognosis. …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…The conidia suspension of AT13 strain (1×106 conidia/mL) was spread on PDA plates overlaid with sterile cellophane, and incubated at 25 °C under darkness. …”