Image 3_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.tif by Liyan Zhao (340225)

“…UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. …”

Table 1_Associations between metabolic-inflammatory biomarkers and Helicobacter pylori infection: an interpretable machine learning prediction approach.docx by Yue Zhang (30585)

“…In the external Chinese cohort, the TyG association attenuated (P = 0.057), but higher TyG/HDL-C quartiles remained significant. Among 11 algorithms, Random Forest (RF) and Gaussian Process (GP) achieved the highest AUCs on the training set (both 0.97) but dropped markedly on the validation set (both 0.75), indicating overfitting. …”

Table 6_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.csv by Liyan Zhao (340225)

“…UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. …”

Table 1_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.csv by Liyan Zhao (340225)

“…UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. …”

Image 5_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.tif by Liyan Zhao (340225)

“…UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. …”

Table 7_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.csv by Liyan Zhao (340225)

“…UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. …”

Table 4_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.csv by Liyan Zhao (340225)

“…UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. …”

Image 2_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.tif by Liyan Zhao (340225)

“…UMAP plots from single-cell RNA sequencing data were used to analyze the expression patterns of hub genes, particularly focusing on macrophage-like SMCs in SMC lineage-traced mouse models. …”

Primer sequences of <i>Bm</i>x and β-actin. by Tianbao Feng (21722233)

“…Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and 8 machine learning algorithms were used to narrowed down hub genes. <i>BMX</i> and <i>CASP5</i> were consistently identified across all algorithms. …”

Performance evaluation of SpaVGN on melanoma ST dataset. by Haiyan Wang (25821)

“…Color-coded regions correspond to different tissue domains. …”

Monotone Cubic B-Splines with a Neural-Network Generator by Lijun Wang (176511)

“…We evaluate our method against several existing methods, some of which do not use the monotonicity constraint, on some monotone curves with varying noise levels. We demonstrate that our method outperforms the other methods, especially in high-noise scenarios. …”

Massive Mixed Models in Julia by Phillip M. Alday (2814652)

“…<p dir="ltr">Traditional approaches to mixed effects models using generalized least squares or expectation-maximization approaches struggle to scale to datasets with many thousands of observations and hundreds of levels of a single blocking variable. Special casing of nesting or crossing of random effects is required to achieve acceptable computational performance, but this special casing often makes it very difficult to handle less-than-idealized cases, such partial crossing or multiple levels of nesting. …”

From GIS to HBIM and Back: Multiscale Performance and Condition Assessment for Networks of Public Heritage Buildings and Construction Components by Teresa Fortunato (21076099)

“…GIS-BIM data exchange routines by programming codes and algorithms are developed in Python. Dynamo “As-built” and “as-damaged” HBIM models are integrated in GIS environment multi-data seismic vulnerability assessment</p>…”