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The Comparative Toxicogenomics Database (CTD) is a public website and research tool that curates scientific data describing relationships between chemicals, genes/proteins, diseases, taxa, phenotypes, GO annotations, pathways, and interaction modules, launched on November 12, 2004.
CTD (Comparative Toxicogenomics Database): describes chemical-gene-disease interactions DiProDB : a database to collect and analyse thermodynamic, structural and other dinucleotide properties Housekeeping and Reference Transcript Atlas (HRT Atlas) [ 27 ] web-based tool for searching cell specific candidate reference genes/transcripts suitable ...
Carbohydrate Structure Database Zelinsky Institute of Organic Chemistry: carbohydrates structures references CSDB ID "CSDB". CTD Comparative Toxicogenomics Database: Department of Biological Sciences at North Carolina State University MeSH CASNo ChEBI PubChem genes, pathways "CTD". DDB Dortmund Data Bank: pure compounds, mixtures, gas hydrates
Reactome is a free online database of biological pathways. [1] [2] [3] ... Comparative Toxicogenomics Database This page was last edited on 9 July 2024, at ...
The Comparative Toxicogenomics Database, helps to understand about the effects of environmental compounds on human health by integrating data from curated scientific literature to describe biochemical interactions with genes and proteins, and links between diseases and chemicals, and diseases and genes or proteins. [11]
In pharmaceutical research, toxicogenomics is defined as the study of the structure and function of the genome as it responds to adverse xenobiotic exposure. It is the toxicological subdiscipline of pharmacogenomics, which is broadly defined as the study of inter-individual variations in whole-genome or candidate gene single-nucleotide polymorphism maps, haplotype markers, and alterations in ...
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The DisGeNET database integrates over 400 000 associations between > 17 000 genes and > 14 000 diseases from human to animal model expert curated databases with text mined GDAs from MEDLINE using a NLP-based approach. [4] The highlights of DisGeNET are the data integration, standardisation and a fine-grained tracking of the provenance information.