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 4_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

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. …”

Table 1_Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.xlsx by Lijuan Feng (3746086)

“…Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. …”

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. …”

<b>dGenhancer v2</b>: A software tool for designing oligonucleotides that can trigger gene-specific Enhancement of Protein Translation. by Adam Master (20316450)

“…Prediction of total Gibbs energies (ΔG=ΔH–TΔS) of the 5’UTR structures can be performed using RNAstructure version 5.2. ΔGs are input data for final dGenhancer calculations as shown by Master A et al 2016¹</p><p dir="ltr"> The algorithms of the calculator were constructed to visualize ΔG changes after <i>in silico</i> introduced single nucleotide substitutions (SNPs) of the 5’UTR sequences. …”