The Comparative Toxicogenomics Database: update 2017

By Allan Peter Davis, Cynthia J. Grondin, Robin J. Johnson, Daniela Sciaky, B. King, Roy McMorran, Jolene Wiegers, Thomas C. Wiegers, Carolyn J. Mattingly. 2016

246 citations recorded by the source corpus.

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.

Subfields: Computational Theory and Mathematics, Molecular Biology, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Computational Biology & Bioinformatics

Citations in the corpus

Snorkel: Rapid Training Data Creation with Weak Supervision (2017, methodology): “We used the Comparative Toxicogenomics Database (CTD) [33] for distant supervision, and additionally wrote labeling functions capturing language patterns and information from the context hierarchy.”
Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning (2021, background): “In the real world, heterogeneous information network (HIN) or heterogeneous graph (HG) [30] is ubiquitous, due to the capacity of modeling various types of nodes and diverse interactions between them, such as bibliographic network [15], biomedical network [3] and so on.”
The Comparative Toxicogenomics Database: update 2019 (2018, background): “3 million manually curated chemical–gene, chemical–phenotype, chemical–disease, gene–disease and chemical–exposure interactions for 15 681 chemicals, 46 689 genes, 4340 phenotypes and 7212 diseases (Table 1), representing a 46% increase in content since our last update (5).”
PaperRobot: Incremental Draft Generation of Scientific Ideas (2019, background): “We apply an entity mention extraction and linking system (Wei et al., 2013) to extract mentions of three entity types ( Disease , Chemical and Gene ) which are the core data categories in the Comparative Toxicogenomics Database (CTD) (Davis et al., 2016), and obtain a Medical Subject Head-ings (MeSH”
Snuba: Automating Weak Supervision to Label Training Data (2018, methodology): “For CDR, users relied on distant supervision through the Comparative Toxicogenomics Database [10].”
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data (2016, methodology): “The CDR task relied on user-deﬁned heuristics and distant supervision from the Comparative Toxicogenomics Databse (CTD) [8] knowledge base.”
DrugBank 5.0: a major update to the DrugBank database for 2018 (2017, methodology): “Additional pharmacoproteomic and pharmacotranscriptomic data were obtained from the CTD (18).”
Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis (2019, methodology): “We performed Gene Set Enrichment Analysis to determine whether integrated single-cell RNA-Seq data can recapitulate gene expression signatures associated with pulmonary ﬁ brosis in the literature using the Comparative Toxicogenomics Database Pulmonary Fibrosis Gene Set (24 – 26).”
The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species (2016, methodology): “Currently, available human data associates ∼ 51% of known human coding genes with phenotype data (based on OMIM (1), ClinVar (2), Orphanet (3), CTD (4) and the GWAS catalog (5)).”