H9-lgDel: Functional Enrichment Clusters for Prader-Willi Syndrome Research

5.5 KB dataset from figshare, last updated 2026-04-28, containing results from a network-based analysis of gene expression in Prader-Willi Syndrome (PWS). Author Kunal Bham mapped PWS expression data onto a human protein-protein interaction network using graph learning to identify impacted proteins and altered biological processes. The analysis suggests abnormalities in glycosylation pathways and homeobox protein regulation linked to PWS phenotypes.

Use Cases

• Validate network-based disease mechanism hypotheses based on identified candidate proteins and functional enrichment clusters.
• Investigate the role of glycosylation pathways (e.g., mucin-type O-glycosylation, fucosylation) in Prader-Willi Syndrome phenotypes.
• Study the overrepresentation of homeobox proteins, particularly those driving GABAergic and dopaminergic neuron development, in relation to PWS.
• Apply or adapt the GeneEMBED methodology described for analyzing other complex diseases with protein-protein interaction networks.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Results are described as highly reproducible across PWS model systems.
• Analysis is based on integrating expression data with a human protein-protein interaction network.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• Description metadata is limited; actual data structure and field semantics must be inferred after download.

Provenance

Source

figshare, author Kunal Bham.

Collection Method

Gene expression data from Prader-Willi Syndrome models was mapped onto a human protein-protein interaction network, and graph learning (GeneEMBED) was used to calculate node embeddings and identify differentially impacted proteins.

Freshness

Last updated 2026-04-28 17:24:06; freshness should be verified.