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.…”

Image 5_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.…”

Image 1_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tiff 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 7_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 4_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 6_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.…”

Image 4_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.…”

Data Sheet 1_Using deep learning to detect upper limb compensation in individuals post-stroke using consumer-grade webcams—A feasibility study.pdf 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 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 1_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. …”

Table 1_Trajectories of health conditions predict cardiovascular disease risk among middle-aged and older adults: a national cohort study.docx by Wenlong Li (571749)

“…Ten machine learning (ML) algorithms were applied to evaluate the predictive capacity of different variable groups for CVD. …”

Image2_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif by Qingqing Long (845156)

“…CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. …”

Image1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif by Qingqing Long (845156)

“…CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. …”

DataSheet1_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.docx by Qingqing Long (845156)

“…CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. …”

Table 3_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. …”