CTD contains direct and inferred chemical–disease relationships. Direct chemical–disease relationships are curated from the published literature by CTD curators.

Inferred relationships are established via CTD-curated chemical–gene interactions (e.g., chemical A is associated with disease B because chemical A has a curated interaction with gene C, and gene C has a direct relationship with disease B).

The following data is presented for this disease:

• The chemical with a direct or inferred relationship with the disease (either the subject of this detail page or one of its descendants).
• The disease associated with each chemical.
• Direct evidence for the chemical–disease relationship (M marker/mechanism and/or T therapeutic)
• The genes on which the inferred relationship between a chemical and disease are based (i.e., genes that have curated interactions with the chemical and direct relationships with the disease).
• The score for the inference based on the topology of the network consisting of the chemical, disease and one or more genes used to make the inference (see below).
• Link to the source reference(s) for the curated and inferred relationships.

The inference score reflects the degree of similarity between CTD chemical–gene–disease networks and a similar scale-free random network. The higher the score, the more likely the inference network has atypical connectivity.

Many biological networks, such as disease and metabolic networks, have been shown to be scale-free random networks.[1] The inference score is calculated as the log-transformed product of two common-neighbor statistics used to assess the functional relationships between proteins in a protein–protein interaction network.[2] The first statistic takes into account the connectivity of the chemical and disease along with the number of genes used to make the inference. The second statistic takes into the account the connectivity of each of the genes used to make the inference.

You may sort these data by clicking on the column headings.

You may save these data into a comma-separated values (CSV) file, XML file or tab-separated values (TSV) file by clicking on the corresponding links at the bottom of the table. CSV and TSV files may be opened using Microsoft Excel.

Top Footnotes

[1]

Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999 Oct 15;286(5439):509-12. [PubMed:10521342]

[2]

Li H, Liang S. Local network topology in human protein interaction data predicts functional association. PLoS One. 2009 Jul 29;4(7):e6410. [PubMed:19641626]