DESTAF: A database of text-mined associations for reproductive toxins potentially affecting human fertility

Adam S. Dawe, Aleksandar Radovanovic, Mandeep Kaur, Sunil Sagar, Sundararajan V. Seshadri, Ulf Schaefer, Allan A. Kamau, Alan Christoffels, Vladimir B. Bajic*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

The Dragon Exploration System for Toxicants and Fertility (DESTAF) is a publicly available resource which enables researchers to efficiently explore both known and potentially novel information and associations in the field of reproductive toxicology. To create DESTAF we used data from the literature (including over 10. 500 PubMed abstracts), several publicly available biomedical repositories, and specialized, curated dictionaries. DESTAF has an interface designed to facilitate rapid assessment of the key associations between relevant concepts, allowing for a more in-depth exploration of information based on different gene/protein-, enzyme/metabolite-, toxin/chemical-, disease- or anatomically centric perspectives. As a special feature, DESTAF allows for the creation and initial testing of potentially new association hypotheses that suggest links between biological entities identified through the database.DESTAF, along with a PDF manual, can be found at http://cbrc.kaust.edu.sa/destaf. It is free to academic and non-commercial users and will be updated quarterly.

Original languageEnglish
Pages (from-to)99-105
Number of pages7
JournalReproductive Toxicology
Volume33
Issue number1
DOIs
Publication statusPublished - Jan 2012
Externally publishedYes

Bibliographical note

Funding Information:
SVS and AC have been supported by the South African Department of Science and Technology/National Research Foundation (DST/NRF) Research Chair in Bioinformatics and Human Health .

Keywords

  • Data-mining
  • Database
  • Developmental toxicology
  • Hypothesis generation
  • Integration systems
  • Reproductive toxicology
  • Text-mining
  • Toxicogenomics

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