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.…”

BioSCape Processed Training Dataset by Alanna Rebelo (17834777)

“…The dataset was prepared according to the following pre-agreed criteria:</p><ul><li>As many points as possible were collected</li><li>The classes needed to be even (same number of training points) for the machine learning algorithms</li><li>Points didn’t need to be paired (i.e. paired invasive alien tree and fynbos points)</li><li>It was not necessary to collect training data in all sampling units, though a general effort to avoid bias and to sample across different sampling units was attempted</li></ul><p></p>…”

Data Sheet 1_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Differentially expressed genes (DEGs) were identified using the limma package (log2FC>0.656, p<0.05). Protein-protein interaction (PPI) networks were constructed using the STRING database. …”

Data Sheet 5_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.csv by Wentong Li (492392)

“…Differentially expressed genes (DEGs) were identified using the limma package (log2FC>0.656, p<0.05). Protein-protein interaction (PPI) networks were constructed using the STRING database. …”

Supplementary file 1_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.docx by Wentong Li (492392)

“…Differentially expressed genes (DEGs) were identified using the limma package (log2FC>0.656, p<0.05). Protein-protein interaction (PPI) networks were constructed using the STRING database. …”

Data Sheet 2_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Differentially expressed genes (DEGs) were identified using the limma package (log2FC>0.656, p<0.05). Protein-protein interaction (PPI) networks were constructed using the STRING database. …”

Data Sheet 3_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Differentially expressed genes (DEGs) were identified using the limma package (log2FC>0.656, p<0.05). Protein-protein interaction (PPI) networks were constructed using the STRING database. …”

Data Sheet 4_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Differentially expressed genes (DEGs) were identified using the limma package (log2FC>0.656, p<0.05). Protein-protein interaction (PPI) networks were constructed using the STRING database. …”

Table 5_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 3_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 6_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 2_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”

Table 4_MMPred: a tool to predict peptide mimicry events in MHC class II recognition.xlsx by Filippo Guerri (17017524)

“…<p>We present MMPred, a software tool that integrates epitope prediction and sequence alignment algorithms to streamline the computational analysis of molecular mimicry events in autoimmune diseases. …”