This Topical Collection focuses on exploring the relationship between the toxicity of xenobiotics and their chemical structures, disturbed cellular, and molecular pathways by the application of artificial intelligent methods. Recent advances in biotechnologies have produced big toxicological data and require advanced artificial intelligence technologies to evaluate the potential for predicting toxicity. Conventional machine learning algorithms and deep learning techniques have been used to process datasets from high-dimensional gene expression, image and high-throughput screening, and chemical structures. These techniques have largely enhanced our capability to recover useful knowledge from the increasing volume of toxicity data and demonstrated a superior predictive performance to conventional machine learning algorithms.
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