Image1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif by Qingqing Long (845156)

“…CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. …”

DataSheet1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.docx by Qingqing Long (845156)

“…CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. …”

Table 3_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. …”

Table 4_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.docx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. …”

Table 5_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.docx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. …”

Table 1_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. …”

Table 2_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. …”

Data Availability for Barrier Island Response to Energetic Storms: a Global View by Valeria Fanti (14857549)

“…As wave direction is a circular variable, in order to allow its use in correlation analysis it was linearized with the sine function and referenced to 270°. This results in negative values (until -1) for storms approaching from the north and positive values (until 1) for storms from the south.…”

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. …”

Table 1_Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning.xlsx by Yuyun Jia (21604337)

“…The intersection of shared DEGs across both conditions and WGCNA-identified genes was determined and subjected to functional enrichment analysis. …”

Image 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.jpeg by Duo Wang (1787634)

“…</p>Results<p>We identified 151 GPR-related genes at both the single-cell and bulk transcriptome levels, and identified a Stepglm[both]+Enet[alpha=0.6] model with seven GPR-related genes as the final diagnostic predictive model with high accuracy and translational relevance using a 127-combination machine learning computational framework, and the GPR-integrated diagnosis nomogram provided a quantitative tool in clinical practice. …”

Table 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.xlsx by Duo Wang (1787634)

“…</p>Results<p>We identified 151 GPR-related genes at both the single-cell and bulk transcriptome levels, and identified a Stepglm[both]+Enet[alpha=0.6] model with seven GPR-related genes as the final diagnostic predictive model with high accuracy and translational relevance using a 127-combination machine learning computational framework, and the GPR-integrated diagnosis nomogram provided a quantitative tool in clinical practice. …”

Turkish_native_goat_genotypes by Yalçın YAMAN (20209833)

“…Partial overlap with mixed linear models and genome-wide McNemar tests suggested that both additive and potential nonlinear components contribute to the observed signal.…”