Environmental Toxicity Identification, Prediction, and Exploration Using Machine Learning: Problems and Perspectives
Received Date: Jan 03, 2023 / Published Date: Jan 30, 2023
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
Citation: Collins M (2023) Environmental Toxicity Identification, Prediction, and Exploration Using Machine Learning: Problems and Perspectives. Toxicol 天美传媒 Access 9: 199. Doi: 10.4172/2476-2067.1000199
Copyright: © 2023 Collins M. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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