Table 10_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 1_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 8_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 9_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xls by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 2_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 5_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

Table 3_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx by Hui Miao (143177)

“…In the RNA binding protein (RBP) -mRNA regulatory network, seven pathways were co-enriched in both biomarkers. In the TF-mRNA regulatory network, TFs shared by both biomarkers include JUN, GATA2, and SRF. …”

GameOfLife Prediction Dataset by David Towers (12857447)

“…Excluding 0, the lower numbers also get increasingly unlikely, though more likely than higher numbers, we wanted to prevent gaps and therefore limited to 25 contiguous classes</p><p dir="ltr">NumPy (.npy) files can be opened through the NumPy Python library, using the `numpy.load()` function by inputting the path to the file into the function as a parameter. …”

Data_Sheet_3_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

“…Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. …”

Data_Sheet_4_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

“…Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. …”

Data_Sheet_2_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

“…Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. …”

Data_Sheet_1_Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning... by Zheng Luo (284622)

“…Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. …”

PR curves of improved YOLOv5s. by Tianyu Cheng (5075000)

“…Experimental results show that the mAP@0.5% of improved YOLOv5s algorithm increases from 92.7% to 99.3%, which means 6.6% accuracy improvement compared with the YOLOv5s model. …”

Table 1_Advancing breast cancer biomarkers: a centromere-related gene signature integrated with single-cell analysis for prognostic prediction.docx by Ye Lu (138787)

“…Additionally, the biological function of the key molecule MMP1 was validated through both in vitro and in vivo experiments.…”

The improved DeepSORT framework. by Tianyu Cheng (5075000)

“…Experimental results show that the mAP@0.5% of improved YOLOv5s algorithm increases from 92.7% to 99.3%, which means 6.6% accuracy improvement compared with the YOLOv5s model. …”

YOLOv5s model with attention modules. by Tianyu Cheng (5075000)

“…Experimental results show that the mAP@0.5% of improved YOLOv5s algorithm increases from 92.7% to 99.3%, which means 6.6% accuracy improvement compared with the YOLOv5s model. …”

Table 1_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

Table 3_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

Table 2_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)

Table 4_Development and evaluation of a machine learning model for post-surgical acute kidney injury in active infective endocarditis.xlsx by XinPei Liu (16560699)