Experimental training parameters. by Rui He (824726)

“…Experimental results demonstrate that, compared with the baseline YOLO11 model, AC-YOLO reduces parameter count by 30.0% and computational load by 15.6% on the SSDD dataset, with an average precision (AP) improvement of 1.2%; on the HRSID dataset, the AP increases by 1.5%. …”

Identification and validation of parthanatos-related genes in end-stage renal disease by Xuan Dai (5153786)

“…Notably, there is a strong correlation between HBM and MYL4 with Type 17 T helper cells and central memory CD4 T cells RT-qPCR validation showed that the expression of biomarkers in ESRD patients was significantly higher than that in controls (<i>p</i> < 0.05).</p> <p>This study identified two biomarkers (HBM, MYL4) through transcriptome analysis, investigating their functions and mechanisms, offering new therapeutic insights for ESRD.…”

Supplementary file 2_Construction and validation of a machine learning model integrating ultrasound features and inflammatory markers (OVART-ML) for predicting ovarian torsion and... by Zhifei Zhao (4014386)

“…This tool may assist clinicians in making timely surgical decisions to preserve ovarian function in children.</p>…”

Structures of C2PSA and C3K2 modules. by Rui He (824726)

“…Experimental results demonstrate that, compared with the baseline YOLO11 model, AC-YOLO reduces parameter count by 30.0% and computational load by 15.6% on the SSDD dataset, with an average precision (AP) improvement of 1.2%; on the HRSID dataset, the AP increases by 1.5%. …”

P values for gene set mRNA enrichment analysis. by Wenliang Yuan (7842908)

“…</p><p>Methods</p><p>Using machine learning algorithms, we identified and analyzed two immune subtypes (C1 and C2) in 244 LNM-negative CRC samples, with validation in 458 additional samples, and evaluated their immune characteristics and functional pathways.…”

The result of differential expression analysis. by Wenliang Yuan (7842908)

“…</p><p>Methods</p><p>Using machine learning algorithms, we identified and analyzed two immune subtypes (C1 and C2) in 244 LNM-negative CRC samples, with validation in 458 additional samples, and evaluated their immune characteristics and functional pathways.…”

The abundances of 13 immune-related cells. by Wenliang Yuan (7842908)

“…</p><p>Methods</p><p>Using machine learning algorithms, we identified and analyzed two immune subtypes (C1 and C2) in 244 LNM-negative CRC samples, with validation in 458 additional samples, and evaluated their immune characteristics and functional pathways.…”

Genetic mutation analysis of GSE39582 dataset. by Wenliang Yuan (7842908)

“…</p><p>Methods</p><p>Using machine learning algorithms, we identified and analyzed two immune subtypes (C1 and C2) in 244 LNM-negative CRC samples, with validation in 458 additional samples, and evaluated their immune characteristics and functional pathways.…”

ICI scores for each sample in TCGA and GSE39582. by Wenliang Yuan (7842908)

“…</p><p>Methods</p><p>Using machine learning algorithms, we identified and analyzed two immune subtypes (C1 and C2) in 244 LNM-negative CRC samples, with validation in 458 additional samples, and evaluated their immune characteristics and functional pathways.…”

Image 5_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. …”

Image 4_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. …”

Image 2_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. …”

Image 3_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.jpeg by Tim Unger (20457145)

“…Over half of stroke survivors suffer from upper limb impairments, making assessments of sensory-motor function crucial for both improving interventions and tracking progress. …”

Image 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.…”

Table 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.…”

Table 8_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.…”

Table 2_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.…”

Image 2_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.…”

Table 6_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.…”

Table 5_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Ion channel-related genes were extracted from microarray datasets and analyzed for differential expression and functional relevance to AF pathology. Machine learning algorithms (LASSO and SVM) were used to identify signature genes from ion channels in AF, followed by drug-enrichment analysis to explore potential therapeutic options.…”