Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles.

ABSTRACT: A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms' response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gen

SUBMITTER: Choi TJ 

PROVIDER: S-EPMC9503646 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature