Diabetes Data and Hypothesis Hub (D2H2)

A platform that facilitates data-driven hypothesis generation for the diabetes and related metabolic disorder research community.
Automated daily hypothesis art
D2H2 Automated Daily Hypothesis
D2H2 identified a significant overlap between gene sets extracted from

...

and

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despite highly dissimilar abstracts. Read more about a plausible explanation about why such a high overlap exists.
D2H2 Chatbot

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Examples
In what tissues is STAT3 expressed?
Which D2H2 studies were conducted in mice and focus on islets?
Which diabetes-related GEO signatures up- or down-regulate the expression of AKT1?
Which LINCS L1000 small molecules may reverser or mimic my up and down gene sets?
Which diabetes signatures significantly overlap with my gene set?
Which genes are most correlated with NFATC1?
Which published gene sets extracted from PubMed Central (PMC) articles significantly overlap with my gene set?
Which D2H2 studies focus on type 2 diabetes?
Which transcription factors regulate my gene set?

Curated transcriptomics datasets from various Type 2 Diabetes studies are made available for download, visualization, and enrichment analysis.

Processed Bulk RNA-seq, Microarray, and scRNA-seq Studies
Human
162
Mouse
177

Find knowledge about a single gene, including in which diabetes-related GEO studies a chosen gene is most differentially expressed.

Find knowledge about a gene set, including performing enrichment analysis agasint our curated diabetes gene set library.

Explore a collection of curated diabetes related Bulk-RNA-seq and microarray studies manually extracted and processed from NCBI's GEO. The expression level of single genes across conditions is visualized as a customized box plot viewer; the samples can be visualized as UMAP, tSNE and PCA plots; and users cam compute differential expression for any two conditions with replicates.

Explore a collection of curated diabetes related scRNA-seq studies extracted from NCBI's GEO. The expression of single genes across clusters of a chosen profile can be visualized as a customized box plot viewer; the single cells can be visualized in a UMAP, t-SNE and PCA; and users can compute differential expression for each automatically identified cluster