Image 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. …”

Image 3_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. …”

Table 1_Trajectories of health conditions predict cardiovascular disease risk among middle-aged and older adults: a national cohort study.docx by Wenlong Li (571749)

“…Ten machine learning (ML) algorithms were applied to evaluate the predictive capacity of different variable groups for CVD. …”

Image2_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. …”

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.…”

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…., 2022a), to estimate the spatiotemporal dynamics of SOC in different soil layers and further evaluate the impacts of different climate response functions on SOC estimates in the Qinling Mountains. …”

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. …”