Exploring the spatial architecture of the human dorsolateral prefrontal cortex (DPLFC). حسب Huamei Li (8815955)

Exploring the spatial architecture of the human thymus. حسب Huamei Li (8815955)

Spatial characterization and cell-type-specific genes in mouse brain tissue. حسب Huamei Li (8815955)

Cell2Spatial enables versatile applications across various analytical domains. حسب Huamei Li (8815955)

Performance assessment of Cell2Spatial on real and simulated mouse brain spatial transcriptomics (ST) data. حسب Huamei Li (8815955)

Comparative analysis of spatial architectures and cellular proportions in Lung, Breast, and Intestinal tissues recovered by different mapping tools. حسب Huamei Li (8815955)

Investigating the spatial architecture of the mouse kidney. حسب Huamei Li (8815955)

Spatial transcriptomics (ST) and single-cell atlases of multiple human tissues. حسب Huamei Li (8815955)

Performance assessment of Cell2Spatial in real ST data from various tissue types. حسب Huamei Li (8815955)

Evaluation of spatial transcriptomics (ST) tools across various metrics and conditions. حسب Huamei Li (8815955)

Secondary search process based on ‘seed’ article used to generate the citation map, adapted from Costa et al. (2025) [48]. حسب Ana Michell García Varela (22574587)

"…<p>Note: The black element in the center represents a published study and was selected as the pivot for mapping the evidence; the articles represented by the orange elements – previous studies (R1, R2, and R5) were directly cited by the seed; the articles represented by the green elements are more recent studies (C1, C3, and C4) that cite the seed; and the lighter-colored elements represent studies that resemble the seed, identified as relevant by an artificial intelligence algorithm using the seed as a reference (R3, R4, C2, and C5, linked by dashed lines). …"

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' حسب Fanzhi Su (21005174)

DataSheet1_DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network.PDF حسب Minwoo Pak (6842618)

"…DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. …"

Ablation study visualization results. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

Experimental parameter configuration. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

FLMP-YOLOv8 identification results. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

C2f structure. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

Experimental environment configuration. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

Ablation experiment results table. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"

YOLOv8 identification results. حسب Xiaozhou Feng (2918222)

"…Experimental results on a self-constructed dataset demonstrate the improved model efficacy, achieving 92.0% precision, 80.8% recall, 87.0% mean Average Precision (mAP@0.5), and 81.79 FPS detection speed. Compared to the original YOLOv8 model, the improved algorithm shows increases of 2.2% in precision, 0.6% in recall, and 2.0% in mAP@0.5, with a detection speed improvement of 65.48 FPS. …"