Identification and validation of hub DE-podos in bulk RNA-seq. by Hailin Li (453832)

Workflow of the study. by Hailin Li (453832)

Unveiling cellular heterogeneity through podocyte subcluster analysis. by Hailin Li (453832)

Power quality control algorithms for small scale power intergration systems by Advocate Mlamla (20170815)

“…A right shunt UPQC has been proposed as a combination of the two devices connected back-to-back through the DC link side for voltage and current multiple power quality issues. Optimisation of effective compensation for the CPDs depends on the proper selection of control algorithms for the gate switching of the Voltage Source Converters (VSCs) used. …”

<i>slc3a2a</i>^<i>zion</i> is a novel regulator of B-lp metabolism. by McKenna Feltes (11242085)

Data Sheet 1_An individualized risk prediction tool for ectopic pregnancy within the first 10 weeks of gestation based on machine learning algorithms.docx by Xin Du (208780)

“…</p>Conclusion<p>This study employed the CatBoost algorithm to develop an individualized risk prediction model by integrating multiple features from the initial visit. …”

Block structure location in the Yueman area. by Chen Ma (714759)

“…Seismic attributes that were sensitive to different types of strike-slip faults were selected, and multiple attributes were merged to obtain a fracture distribution map using the best surface voting algorithm. …”

Normalized convergence time. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

VGR structure. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Comparison of normalized throughput and load. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Principle of transfer learning. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Body-connected routing scenario. by Song Qian (5031221)

“…The traditional artificial intelligence routing algorithm cannot deal with the low model prediction accuracy and poor generalization ability caused by large noise and small data volume. …”

Table 1_An interpreting machine learning models to predict amputation risk in patients with diabetic foot ulcers: a multi-center study.doc by Haoran Tao (8466261)

“…Data from 297 patients across multiple tertiary centers were used for external validation. …”

Table 2_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”

Table 8_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”

Table 1_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”

Table 4_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”

Table 5_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”

Table 6_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”

Table 7_Serum metabolomics-based diagnostic biomarkers for colorectal cancer: insights and multi-omics validation.xlsx by Taorui Wang (22300702)

“…A metabolomics-based diagnostic model built using ten selected metabolites demonstrated excellent discriminatory performance, achieving area under the receiver operaring characteristic curve (AUROC) of 0.96-0.97 and accuracies up to 92.5% across multiple machine learning methods. …”