ctd_associations — Comparative Toxicogenomics Database associations (CTD, 2026)

Description

Bipartite networks of chemicals, genes and diseases from the Comparative Toxicogenomics Database (CTD), which curates from the scientific literature how environmental chemicals and drugs interact with genes and proteins, and how chemicals and genes are related to diseases, in many species. The data are those of the release of September 2026.

chemical_disease: chemicals and diseases. An edge is either a curated association from the literature, with its direct evidence (marker/mechanism: the chemical correlates with the disease or may play a role in its etiology; therapeutic: the chemical has a known or potential therapeutic role in the disease), or an association inferred by CTD through a gene that has curated interactions with the chemical and a curated association with the disease, with the inference gene and an inference score (higher for better connected networks of chemicals, genes and diseases). A chemical and a disease are connected by one edge per inference gene, and by an edge per kind of direct evidence.
chemical_gene: chemicals and genes. An edge is a curated interaction between a chemical and a gene product (or the gene itself) in an organism, e.g. "Aspirin results in decreased expression of PTGS2 mRNA", with the interaction actions (e.g. decreases^expression); the genes are identified by their NCBI Gene identifier, irrespective of the organism of the interaction (CTD maps the genes of all organisms to the identifiers of the genes it lists, mostly of vertebrates).
gene_disease: genes and diseases, with an edge for each curated association (marker/mechanism or therapeutic) of a gene with a disease.
gene_disease_inferred: genes and diseases, with an edge for each association inferred by CTD through chemicals that have curated interactions with the gene and curated associations with the disease, with the inference chemicals and the inference score of the gene-disease pair. CTD lists the association once per inference chemical (130 million rows in the release of September 2026); these are merged into one edge per gene-disease pair (about 36 million).

The diseases are terms of the "merged disease vocabulary" (MEDIC) of CTD, which combines the MeSH disease terms and OMIM; the chemicals are MeSH terms. The chemical-pathway, gene-pathway, disease-pathway, chemical-phenotype and exposure data of CTD are not included.

The networks have the following properties:

(Node) ctd_id: the identifier of the node: the MeSH identifier of the chemical (e.g. D001241, aspirin), the NCBI Gene identifier of the gene (e.g. 5743), or the MeSH or OMIM identifier of the disease (e.g. MESH:D003920, OMIM:125853).
(Node) name: the name of the chemical, the symbol of the gene, or the name of the disease.
(Node) cas_rn: the CAS Registry Number of the chemical (empty if none, and for other nodes).
(Node) is_chemical (chemical_disease, chemical_gene): whether the node is a chemical.
(Node) is_gene (gene_disease, gene_disease_inferred): whether the node is a gene (otherwise it is a disease).
(Edge) direct_evidence (chemical_disease, gene_disease): the kind of curated evidence, marker/mechanism or therapeutic (empty for inferred associations).
(Edge) inference_gene (chemical_disease): the symbol of the gene through which the association is inferred (empty for curated associations).
(Edge) inference_score (chemical_disease, gene_disease_inferred): the inference score of the association (NaN for curated associations).
(Edge) inference_chemicals (gene_disease_inferred): the names of the chemicals through which the association is inferred, separated by |; num_chemicals, their number.
(Edge) omim_ids (chemical_disease, gene_disease): the OMIM identifiers of the evidence, separated by | (empty if none).
(Edge) pubmed_ids: the PubMed identifiers of the articles that support the association or interaction, separated by | (in gene_disease_inferred, those of all its inference chemicals).
(Edge) organism, organism_id (chemical_gene): the name and NCBI taxonomy identifier of the organism of the interaction (empty and -1 if not given).
(Edge) gene_forms (chemical_gene): the forms of the gene product involved, e.g. protein, mRNA, separated by |.
(Edge) interaction (chemical_gene): the description of the interaction.
(Edge) interaction_actions (chemical_gene): the actions of the interaction, e.g. increases^expression, separated by |.
(Node) inferred_associations (graph, gene_disease): the number of inferred gene-disease associations, which are in gene_disease_inferred.

Tags

Biological Chemical Public Health Unweighted Multigraph Metadata

Added

2026-10-28

Citation

A. P. Davis, T. C. Wiegers, D. Sciaky, F. Barkalow, M. Strong, B. Wyatt, J. Wiegers, R. McMorran, S. Abrar, C. J. Mattingly, "Comparative Toxicogenomics Database's 20th anniversary: update 2025." Nucleic Acids Research 53, D1328-D1334 (2025) 10.1093/nar/gkae883 Sci-Hub

Upstream URL OK

https://ctdbase.org/
https://ctdbase.org/downloads/

Upstream license

CTD legal notices: research and educational use only; no commercial use without permission; cite and link CTD, https://ctdbase.org/about/legal.jsp

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
chemical_disease	25,206	9,903,450	785.80	3774.14	1277.21	4.93	-0.17	8	1.00	Undirected	Bipartite	ctd_id name is_chemical cas_rn	direct_evidence inference_gene inference_score omim_ids pubmed_ids	36.0 MiB	92.5 MiB	94.6 MiB	104.5 MiB
chemical_gene	73,813	3,171,202	85.93	860.72	702.50	8.23	-0.15	8	1.00	Undirected	Bipartite	ctd_id name is_chemical cas_rn	organism organism_id gene_forms interaction interaction_actions pubmed_ids	26.7 MiB	37.2 MiB	36.5 MiB	39.0 MiB
gene_disease	14,985	34,315	4.58	17.32	37.36	31.28	-0.17	14	0.86	Undirected	Bipartite	ctd_id name is_gene	direct_evidence omim_ids pubmed_ids	542 KiB	729 KiB	731 KiB	677 KiB
gene_disease_inferred	61,407	35,690,513	1162.42	3568.91	4881.66	1.25	-0.67	5	1.00	Undirected	Bipartite	ctd_id name is_gene	inference_score num_chemicals inference_chemicals pubmed_ids	554.7 MiB	877.7 MiB	852.4 MiB	1.967 GiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.