Table 1_Identification and experimental validation of demethylation-related genes in diabetic nephropathy.xlsx حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 حسب 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 حسب 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... حسب 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... حسب 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... حسب 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... حسب 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. حسب 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 حسب 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. حسب 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. حسب 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. …"

Code and data for evaluating oil spill amount from text-form incident information حسب Yiming Liu (18823387)

"…These are separately stored in the folders “description” and “posts”.</p><h2>Algorithms for Evaluating Release Amount (RA)</h2><p dir="ltr">The algorithms are split into the following three notebooks based on their functions:</p><ol><li><b>"1_RA_extraction.ipynb"</b>:</li><li><ul><li>Identifies oil spill-related incidents from raw incident data.…"

Supplementary materials for PhD thesis 'Characterisation Of The Blazhko Effect In RR Lyrae Stars Using SuperWASP Data' حسب Paul Greer (22619328)

"…Blazhko periods were calculated for 18 out of 20 highly modulated objects by phase-folding the amplitude modulation induced upper envelope function of their light curves.<br><br>983 Blazhko objects have been identified using coincident low frequency peaks and sidebands, or equidistant sidepeaks, in frequency spectra created using the CLEAN algorithm. …"

Code حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Core data حسب Baoqiang Chen (21099509)

"…We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …"

Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy حسب Han Zhong (5159162)

"…When both the value and policy functions are represented by multi-layer overparameterized neural networks, we prove that our actor-critic algorithm generates a sequence of policies that finds a globally optimal policy at a sublinear rate. …"