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Showing 1 to 4 of 4 for “"computational toxicology"”.

  1. Computational ligand discovery for the human and zebrafish sex hormone binding globulin

    … interactions. Studies applying this new computational toxicology method could increase the awareness of hazards posed by existing commercial chemicals at relatively low cost.

    ubc Repository record for Computational ligand discovery for the human and zebrafish sex hormone binding globulin (opens in a new tab)

  2. Developmental Malformations in Zebrafish Caused by Exposure to Environmental Pollutants

    It is estimated that there are about 80,000 man-made chemicals released to the environment with little to no toxicity information. Exposure to these chemicals is identified as one of the risk factors for causing birth defects. We used zebrafish as a model to identify and predict environmental …

    houston Repository record for Developmental Malformations in Zebrafish Caused by Exposure to Environmental Pollutants (opens in a new tab)

  3. Using transcriptomic data to detect, understand, and treat injury in the context of drug toxicity and fibrotic disease

    In drug discovery, it is crucial to understand how drugs relate to complex phenotypes. This includes understanding how a drug can help to treat a condition, but also how it can result in adverse effects so that safety risks can be mitigated earlier. How effects propagate from the molecular to the …

    cambridge Repository record for Using transcriptomic data to detect, understand, and treat injury in the context of drug toxicity and fibrotic disease (opens in a new tab)

  4. Accurate Uncertainty Quantification and Explainable Artificial Intelligence in Machine Learning Models for Toxicological Risk Assessment

    Consumer and environmental safety decisions can be supported by Quantitative Structure-Activity Relationship (QSAR) models – a key part of the Next Generation Risk Assessment strategy for animal-free safety. Machine learning methods are often employed to build QSAR models, but these “black box” …

    cambridge Repository record for Accurate Uncertainty Quantification and Explainable Artificial Intelligence in Machine Learning Models for Toxicological Risk Assessment (opens in a new tab)