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. …”

Datasheet1_Generalising electrocardiogram detection and delineation: training convolutional neural networks with synthetic data augmentation.zip by Guillermo Jimenez-Perez (13239306)

“…Machine learning (ML) techniques based on deep learning algorithms have emerged as promising alternatives, capable of achieving similar performance without handcrafted features or thresholds. …”