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Help: Gene Diseases

Gene–disease associations may be inferred via curated chemical–gene and chemical–disease associations. CTD contains curated and inferred gene–disease associations. Curated gene–disease associations are curated from the published literature by CTD curators or are derived from the OMIM database using the mim2gene file from the NCBI Gene database.

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

The following data is presented for this gene:

  1. The disease associated with the gene.
  2. Direct evidence for the association (M marker/mechanism and/or T therapeutic)
  3. The chemicals on which the inferred association is based (i.e., chemicals that have curated interactions with the gene and curated associations with the disease).
  4. The score for the inference based on the topology of the network consisting of the gene, disease, and one or more chemicals used to make the inference (see Inference Score, below).
  5. A link to the source reference(s) for the curated and inferred associations.

Inference Score

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 gene and disease along with the number of chemicals used to make the inference. The second statistic takes into the account the connectivity of each of the chemicals used to make the inference.


Sort these data differently by clicking a column heading.


Save these data into a comma-separated values (CSV), Excel, XML, or tab-separated values (TSV) file by clicking a Download link at the bottom of the table.

Top ↑ Footnotes

Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999 Oct 15;286(5439):509-12. PMID:10521342
Li H, Liang S. Local network topology in human protein interaction data predicts functional association. PLoS One. 2009 Jul 29;4(7):e6410. PMID:19641626