Data files

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Biochemical Reaction Networks Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations Kinetic descriptions of biochemical reactions related to each renal cell ...

Enriched pathway landscapes are generated for each cell type using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). Reactome is selected as the pathway ontology. In the resulting graph, nodes correspond to significantly enriched pathways, and edges width is proportional to the number of shared genes between pathway pairs, ...

Protein–protein interaction networks are constructed for each renal cell type to visualize DKD-related molecular connectivity. Nodes represent proteins implicated in diabetic kidney disease (DKD) based on comprehensive literature curation, proteomic profiling, mRNA microarray analyses, and GWAS hits. Edges denote experimentally validated interactions retrieved from the SIGNOR 3.0 database; only interactions for which both source and target belong to the DKD-associated set are retained. Network ...

Biochemical Reaction Networks: Biochemical Reaction Networks transform static protein-protein interactions (PPI) network into dynamic, mechanistic models by decomposing each interaction into underlying molecular events like phosphorylation and complex formation. Using SPADAN toolbox (DOI:10.1093/bioinformatics/btad079), we expands PPI network into biochemical reactions network.

Ordinary Differential Equation Model Equations: Kinetic descriptions of biochemical reactions related to each renal cell ...

Enriched pathway landscapes are generated for each cell type using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). Reactome is selected as the pathway ontology. In the resulting graph, nodes correspond to significantly enriched pathways, and edges width is proportional to the number of shared genes between pathway pairs, ...

Protein–protein interaction networks are constructed for each renal cell type to visualize DKD-related molecular connectivity. Nodes represent proteins implicated in diabetic kidney disease (DKD) based on comprehensive literature curation, proteomic profiling, mRNA microarray analyses, and GWAS hits. Edges denote experimentally validated interactions retrieved from the SIGNOR 3.0 database; only interactions for which both source and target belong to the DKD-associated set are retained. Network ...

Gene Ontology (GO) enrichment is performed using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). The “Biological Process (non-redundant)” GO subset is chosen to minimize annotation overlap. Network nodes denote enriched GO terms, and edges are weighted by the count of common genes, enabling the identification of tightly ...

Enriched pathway landscapes are generated for each cell type using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). Reactome is selected as the pathway ontology. In the resulting graph, nodes correspond to significantly enriched pathways, and edges width is proportional to the number of shared genes between pathway pairs, ...

Protein–protein interaction networks are constructed for each renal cell type to visualize DKD-related molecular connectivity. Nodes represent proteins implicated in diabetic kidney disease (DKD) based on comprehensive literature curation, proteomic profiling, mRNA microarray analyses, and GWAS hits. Edges denote experimentally validated interactions retrieved from the SIGNOR 3.0 database; only interactions for which both source and target belong to the DKD-associated set are retained. Network ...

Gene Ontology (GO) enrichment is performed using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). The “Biological Process (non-redundant)” GO subset is chosen to minimize annotation overlap. Network nodes denote enriched GO terms, and edges are weighted by the count of common genes, enabling the identification of tightly ...

Enriched pathway landscapes are generated for each cell type using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). Reactome is selected as the pathway ontology. In the resulting graph, nodes correspond to significantly enriched pathways, and edges width is proportional to the number of shared genes between pathway pairs, ...

Protein–protein interaction networks are constructed for each renal cell type to visualize DKD-related molecular connectivity. Nodes represent proteins implicated in diabetic kidney disease (DKD) based on comprehensive literature curation, proteomic profiling, mRNA microarray analyses, and GWAS hits. Edges denote experimentally validated interactions retrieved from the SIGNOR 3.0 database; only interactions for which both source and target belong to the DKD-associated set are retained. Network ...

Gene Ontology (GO) enrichment is performed using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). The “Biological Process (non-redundant)” GO subset is chosen to minimize annotation overlap. Network nodes denote enriched GO terms, and edges are weighted by the count of common genes, enabling the identification of tightly ...

Protein–protein interaction networks are constructed for each renal cell type to visualize DKD-related molecular connectivity. Nodes represent proteins implicated in diabetic kidney disease (DKD) based on comprehensive literature curation, proteomic profiling, mRNA microarray analyses, and GWAS hits. Edges denote experimentally validated interactions retrieved from the SIGNOR 3.0 database; only interactions for which both source and target belong to the DKD-associated set are retained. Network ...

Gene Ontology (GO) enrichment is performed using WebGestalt 2024 (DOI:10.1093/nar/gkae456). UniProt identifiers serves as input for an over-representation analysis employing the hypergeometric test with Benjamini–Hochberg false discovery rate (FDR) correction (FDR ≤ 0.05). The “Biological Process (non-redundant)” GO subset is chosen to minimize annotation overlap. Network nodes denote enriched GO terms, and edges are weighted by the count of common genes, enabling the identification of tightly ...