How Can Machine Learning Improve Toxicity Prediction?
Machine learning (ML) has emerged as a powerful tool for improving toxicity prediction. ML algorithms can analyze vast datasets to identify patterns and correlations that traditional methods might miss. Techniques like deep learning, random forests, and support vector machines can enhance the accuracy and reliability of toxicity models. Additionally, ML can integrate diverse data sources, such as chemical properties, biological responses, and environmental factors, to provide a more holistic prediction.
