Multi-view uncertainty deep forest: An innovative deep forest equipped with uncertainty estimation for drug-induced liver injury prediction

Abstract

Drug-induced liver injury (DILI) refers to the harmful effects that certain drugs can have on the liver, potentially leading to severe health issues and even life-threatening conditions. Early detection of potential DILI events is crucial in drug development and toxicity assessment to ensure patient safety and the successful market entry of pharmaceuticals. We introduced a novel model, the Multi-view Uncertainty Deep Forest (MVU-DF), for predicting DILI. This model expands the Deep Forest architecture to multiple views and uses two-stage feature construction: intra-view feature interaction for key information within a view, and inter-view feature interaction for sharing information across views. Additionally, we incorporated uncertainty estimation to evaluate the confidence of predictions, using opinion as an alternative to probability. The opinion measures class support and incorporates uncertainty, enabling more precise differentiation of hard and easy samples in each MVU-DF iteration, enhancing the cascading scheme. The model's high interpretability enhances prediction performance and reliability. Our experiments demonstrate the potential of this research in improving the accuracy and decision-making transparency of hepatotoxicity predictions in drug development.