Big Data in Computational Toxicology: Challenges and Opportunities

Linlin Zhao, Hao Zhu

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations

Abstract

This chapter describes the volume and velocity of the data-driven research for toxicology have been well recognized by multiple screening and data-sharing projects. It describes the hybrid models and new computational approaches to use various types of toxicity data in the computational toxicity field. Big data research will be one of the major efforts of modern toxicology in the future. In the current big data era, all the public toxicity data can be used for profiling toxicants. The use of unstructured toxicity data has motivated the development of text mining approaches in computational toxicity. Traditional read-across approaches of computational toxicology, which were widely used to fill data gaps of new compounds without relevant toxicity data, are usually based on chemical similarity search or QSAR predictions. The recent data generation efforts in the area of toxicology are toxicity forecaster (ToxCast) initiated by the US Environmental Protection Agency (EPA) and Toxicity Testing in the twenty-first century (Tox21).

Original languageEnglish (US)
Title of host publicationComputational Toxicology
Subtitle of host publicationRisk Assessment for Chemicals
Publisherwiley
Pages291-312
Number of pages22
ISBN (Electronic)9781119282594
ISBN (Print)9781119282563
DOIs
StatePublished - Feb 14 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science(all)
  • Pharmacology, Toxicology and Pharmaceutics(all)

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