What are the Challenges in Applying Machine Learning to Toxicology?
Despite its advantages, applying ML to toxicology presents several challenges:
- Data Quality: The accuracy of ML models heavily relies on the quality of training data. Inconsistent or incomplete data can lead to unreliable predictions. - Interpretability: Many ML models, especially deep learning networks, act as "black boxes," making it difficult to interpret their decisions. - Overfitting: Models trained on limited datasets may perform well on training data but fail to generalize to new data. - Computational Resources: Training complex ML models, particularly deep learning networks, requires significant computational power and resources.
