The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
The Comparative Toxicogenomics Database: update 2017
Open accessAbstract
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.
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Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
Self-citationKEGG as a reference resource for gene and protein annotation
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1070 · Cited by 7,387
The Reactome pathway Knowledgebase
Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., Jassal, B., Jupe, S., Korninger, F., McKay, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1351 · Cited by 5,605
Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F., & Hamosh, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1205 · Cited by 2,691
Wild, C. P. (2005) · Cancer Epidemiology, Biomarkers & Prevention · doi:10.1158/1055-9965.epi-05-0456 · Cited by 2,562
The BioGRID interaction database: 2015 update
Chatr-aryamontri, A., Breitkreutz, B.-J., Oughtred, R., Boucher, L., Heinicke, S., Chen, D., Stark, C., Breitkreutz, A., Kolas, N., O'Donnell, L., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1204 · Cited by 890
BioCreative V CDR task corpus: a resource for chemical disease relation extraction
Li, J., Sun, Y., Johnson, R. J., Sciaky, D., Wei, C.-H., Leaman, R., Davis, A. P., Mattingly, C. J., Wiegers, T. C., & Lu, Z. (2016) · Database · doi:10.1093/database/baw068 · Cited by 870
Self-citationKibbe, W. A., Arze, C., Felix, V., Mitraka, E., Bolton, E., Fu, G., Mungall, C. J., Binder, J. X., Malone, J., Vasant, D., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1011 · Cited by 580
The Comparative Toxicogenomics Database: update 2013
Davis, A. P., Murphy, C. G., Johnson, R., Lay, J. M., Lennon-Hopkins, K., Saraceni-Richards, C., Sciaky, D., King, B. L., Rosenstein, M. C., Wiegers, T. C., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks994 · Cited by 497
Self-citationDNorm: disease name normalization with pairwise learning to rank
Leaman, R., Islamaj Doğan, R., & Lu, Z. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt474 · Cited by 484
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Reyfman, P. A., Walter, J. M., Joshi, N., Anekalla, K. R., McQuattie-Pimentel, A. C., Chiu, S., Fernandez, R., Akbarpour, M., Chen, C.-I., Ren, Z., et al. (2019) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201712-2410oc · Cited by 1,203
Comparative Toxicogenomics Database (CTD): update 2021
Davis, A. P., Grondin, C. J., Johnson, R. J., Sciaky, D., Wiegers, J., Wiegers, T. C., & Mattingly, C. J. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa891 · Cited by 1,107
Self-citationMungall, C. J., McMurry, J. A., Köhler, S., Balhoff, J. P., Borromeo, C., Brush, M., Carbon, S., Conlin, T., Dunn, N., Engelstad, M., et al. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1128 · Cited by 441
Deep Learning applications for COVID-19
Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350
Metadata
- Authors
- Davis, A. P., Grondin, C. J., Johnson, R. J., Sciaky, D., King, B. L., McMorran, R., Wiegers, J., Wiegers, T. C., & Mattingly, C. J.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-09-19
- Volume
- 45
- Issue
- D1
- Pages
- D972-D978
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2915806464
- Cited by
- 544
- References
- 35
- FWCI
- 43.04
- Type
- journal article
- Language
- en
- Open access
- gold
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- Davis, A. P., Grondin, C. J., Johnson, R. J., Sciaky, D., King, B. L., McMorran, R., Wiegers, J., Wiegers, T. C., & Mattingly, C. J. (2017). The Comparative Toxicogenomics Database: update 2017. Nucleic Acids Research. https://doi.org/10.1093/nar/gkw838
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
