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Chemical–disease associations may be inferred via curated chemical–gene and gene–disease associations. CTD contains curated and inferred chemical–disease associations.

Curated associations are extracted from the published literature by CTD curators.

Inferred associations are established via 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 curated association with disease B).

The following data is presented for these associations:

  1. The chemical having a curated or inferred association with the disease (either the subject of this detail page or one of its descendants).
  2. The disease associated with each chemical.
  3. Direct evidence for the chemical–disease association (M marker/mechanism and/or T therapeutic)
  4. Links to view the GO functional annotations or pathways that are enriched for the gene set that underlies this chemical–disease association (see Enrichment Analysis, below).
  5. The genes on which the inferred association between a chemical and disease are based (i.e., genes that have curated interactions with the chemical and curated associations with the disease).
  6. 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 Inference Score, below).
  7. Link to the source reference(s) for the curated and inferred associations.

Enrichment Analysis

Diseases Diseases
Many of the genes/proteins with curated chemical interactions in CTD are represented in CTD's MEDIC disease vocabulary. To provide insight into diseases that may be influenced by a chemical, this report provides a list of diseases that are statistically enriched among the genes/proteins that interact with the chemical or its descendants.
GO terms GO terms
Many genes/proteins with curated chemical interactions in CTD have Gene Ontology (GO) annotations that provide information about their associated biological processes, molecular functions, and cellular components. To provide insight into the biological properties that may be affected by a chemical, this report provides a list of GO terms that are statistically enriched among the genes/proteins that interact with the chemical or its descendants.
Pathways Pathways
Many of the genes/proteins with curated chemical interactions in CTD are represented in KEGG and REACTOME pathway maps that represent molecular interaction and reaction networks. To provide insight into the pathways and networks that may be affected by a chemical, this report provides a list of pathways that are statistically enriched among the genes/proteins that interact with the chemical or its descendants.

A disease, GO term, or pathway is considered enriched if the proportion of genes annotated to it in a test set is significantly larger than the proportion of all genes annotated to it in the genome.

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 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.

For more information about inference scores, please refer to our publication:

King BL, Davis AP, Rosenstein MC, Wiegers TC, Mattingly CJ.
Ranking Transitive Chemical-Disease Inferences Using Local Network Topology in the Comparative Toxicogenomics Database.
PLoS One. 2012;7(11):e46524. PMID:23144783

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Top ↑ Footnotes

[1]
Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999 Oct 15;286(5439):509-12. PMID: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. PMID:19641626
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