Skip to main content

Transforming EPA Science to Meet Today's and Tomorrow's Challenges (2023) / Chapter Skim
Currently Skimming:

Appendix F: Data Science and Machine Learning
Pages 140-146

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 140...
... Although QSAR modeling has existed for a long time, it has advanced to a new stage with the recent progress of machine learning to deep learning. However, critical issues exist in the current computational toxicology models, such as reliance on small training sets which cause limited coverage by the resulting models (Stouch et al., 2003)
Read the entire page →
From page 141...
... IDENTIFYING INFLUENCES OF AIR QUALITY ON BIOLOGICAL AIR CONTAMINANTS EPA, through both intramural and extramural research portfolios, has contributed to the development of high-performance computational models of atmospheric photochemistry as it relates to secondary air pollutant formation, tropospheric smog, and the oxidizing species it contains. Although not currently regulated in the United States, the chemical composition of indoor environments including indoor air has increasingly been the subject of scientific research and health-effects exposure studies.
Read the entire page →
From page 142...
... Modeling environmental concentrations based on incomplete monitoring data is aided by GIS-based models that have comprehensive information about river flows at high and low seasonal volumes. These models can be used as the basis for estimating the concentrations of environmental chemicals released from wastewater treatment plants, with the models being validated by environmental sampling and analytical chemistry (McDonough et al., 2016)
Read the entire page →
From page 143...
... 2021. Revealing adverse outcome pathways from public high-throughput screening data to evaluate new toxicants by a knowledge based deep neural network approach.
Read the entire page →
From page 144...
... 2016. Mechanism profiling of hepatotoxicity caused by oxidative stress using antioxidant response element reporter gene assay models and big data.
Read the entire page →
From page 145...
... 2013. Development of an adverse outcome pathway from drug-mediated bile salt ex port pump inhibition to cholestatic liver injury.
Read the entire page →
From page 146...
... 2014. Big data in chemical toxicity research: The use of high-throughput screening assays to identify potential toxicants.
Read the entire page →

Key Terms

  • machine learning
  • outcome pathway
  • chemical toxicity
  • big data
  • indoor air

  • computational models
  • computational toxicology
  • qsar modeling
  • environmental concentrations
  • indoor environment

  • toxicity testing
  • indoor environments
  • inhalation toxicity
  • toxicity evaluations
  • key events

  • modeling frameworks
  • grand vision
  • modeling approaches
  • wastewater treatment
  • atmospheric photochemistry

  • infectious disease
  • outdoor air
  • treatment plants
  • learning approaches
  • secondary air

  • artificial intelligence
  • acute inhalation
  • neural network
  • outcome pathways
  • screening assays


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.