Machine learning is a cornerstone of advanced data analytics in toxicology. Algorithms can process and learn from large datasets, identifying patterns and relationships that may not be evident through traditional analysis. Techniques such as supervised learning, unsupervised learning, and reinforcement learning are utilized to classify chemicals, predict adverse outcomes, and optimize experimental designs.
