Abstract

Data-driven machine learning (ML), which has gained recent popularity in environmental toxicology, has distanced itself from hypothesis-driven research during the past few decades. The application of ML in environmental toxicology is still in its infancy, however, due to knowledge gaps, technical challenges with data quality, interpretability issues with high-dimensional/heterogeneous/small-sample data analysis, and a lack of a thorough understanding of environmental toxicology. We evaluate the most current advancements in the literature and highlight cutting-edge toxicological investigations utilising ML in light of the aforementioned issues (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution).

Keywords: Machine learning; Environmental toxicology; Pollution