Table 5_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 4_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 2_Athero-oncology perspective: identifying hub genes for atherosclerosis diagnosis using machine learning.xls 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 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. …”

EKC virus-specific HMGB1 secretion. by Amrita Saha (1771837)

“…Sequence differences across types are color coded and sequence conservation are shown with dots. …”

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. …”

Table 1_Demethylase FTO mediates m6A modification of ENST00000619282 to promote apoptosis escape in rheumatoid arthritis and the intervention effect of Xinfeng Capsule.docx by Fanfan Wang (1669474)

“…The m6A modification of long non-coding RNAs (lncRNAs) plays a critical regulatory role in RA pathogenesis. …”

<b>Neural Symbolic Vault: Symbolic Species and</b> <b>DNA Co-Encoding Research Bundle v1.0 (A+M[S] Archive)</b> by Jeffrey Siergiej (20937434)

“…Symbolic DNA encoding structures, vector libraries, and derived algorithms are under private protection via in-house symbolic token ledger and mutation tracking systems.…”

Machine vision system for quantification of aortic and pulmonic valvuloplasty catheter compliance by Jiazhe Tang (17596080)

“…Upon ballon inflation, the defocused image is then refocused though passive focusing algorithms used to identify the best focal position. …”

Table 1_Reducing misdiagnosis in AI-driven medical diagnostics: a multidimensional framework for technical, ethical, and policy solutions.docx by Yue Li (102191)

“…The main research question is how technical limitations, ethical concerns, and unclear accountability hinder safe and equitable use of AI in real-world clinical practice, and what integrated solutions can minimize errors and promote trust.</p>Methods<p>We conducted a literature review and case analysis across major medical fields, evaluating failure modes such as data pathology, algorithmic bias, and human-AI interaction. …”

Image 6_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.tif by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”

Data Sheet 1_Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.pdf by Claire R. van Genugten (20626733)

“…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …”

Presentation 1_Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.pptx by Liuqing Yang (128705)

“…Astragalus, Salvia, and Safflower are the primary elements of Xuebijing, commonly used in the clinical treatment of sepsis. …”