The Comparative Toxicogenomics Database: update 2019

By Allan Peter Davis, Cynthia J. Grondin, Robin J. Johnson, Daniela Sciaky, Roy McMorran, Jolene Wiegers, Thomas C. Wiegers, Carolyn J. Mattingly. Nucleic Acids Res., 2018

802 citations recorded by the source corpus.

Abstract The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a premier public resource for literature-based, manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. In this biennial update, we present our new chemical–phenotype module that codes chemical-induced effects on phenotypes, curated using controlled vocabularies for chemicals, phenotypes, taxa, and anatomical descriptors; this module provides unique opportunities to explore cellular and system-level phenotypes of the pre-disease state and allows users to construct predictive adverse outcome pathways (linking chemical–gene molecular initiating events with phenotypic key events, diseases, and population-level health outcomes). We also report a 46% increase in CTD manually curated content, which when integrated with other datasets yields more than 38 million toxicogenomic relationships. We describe new querying and display features for our enhanced chemical–exposure science module, providing greater scope of content and utility. As well, we discuss an updated MEDIC disease vocabulary with over 1700 new terms and accession identifiers. To accommodate these increases in data content and functionality, CTD has upgraded its computational infrastructure. These updates continue to improve CTD and help inform new testable hypotheses about the etiology and mechanisms underlying environmentally influenced diseases.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/41e2692d9ac1434d1841d5a29e7ccb927b82b677

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Citations in the corpus

Graph embedding on biomedical networks: methods, applications and evaluations (2019, neutral): “Finally, we obtain 92 813 edges between 12 765 nodes (9580 chemicals and 3185 diseases) in this graph (named as ‘CTD DDA’).”
Self-Alignment Pretraining for Biomedical Entity Representations (2020, methodology): “In the future, we plan to (1) generalise such technique to the general domain by leveraging knowledge graphs such as DBpedia; (2) test our model on other BioNLP benchmarks (Gu et al., 2020) et al., 2019) chemical dictionary.”
Machine learning applications in drug development (2019, neutral): “Comparative Toxicogenomics Database (CTD) [98], (2019–11)”
Open Graph Benchmark: Datasets for Machine Learning on Graphs (2020, background): “to domains with dense graphs, such as the protein-protein association graphs and drug-drug interaction networks typically found in biology and medicine (Szklarczyk et al., 2019; Wishart et al., 2018; Davis et al., 2019; Szklarczyk et al., 2016; Piñero et al., 2020). Very recently, Sinha et al. (2020”
Computational Approaches in Preclinical Studies on Drug Discovery and Development (2020, background): “CTD is a powerful and public database designed to enhance understanding of how environmental exposures inﬂuence human health.”
Comparative Toxicogenomics Database (CTD): update 2021 (2020, background): “Curated chemical– phenotype content is reciprocally displayed on data-tabs for both the corresponding chemical and phenotype (6,14).”