Active Stacking-Deep Learning with Strategic Sampling for Small and Imbalanced Chemical Toxicity Prediction.

ABSTRACT: Major challenges in toxicity prediction include dealing with imbalanced and limited data sets, especially when evaluating the harmful potential of chemicals. These issues often lead to poor predictive model performance. Stacking ensemble learning enhances performance by combining predictions from multiple base models, enabling the stack model to improve overall generalization. Active learning (AL), on the other hand, reduces the need for large-scale data sets by effectively training models using carefully selected samples. One effective approach to address data imbalance is the use of strategic sampling techniques. Hereby, we introduce an active stacking-deep learning framework that integrates deep neural networks (DNNs), including a convolutional neural network (CNN), a bidirectional long

SUBMITTER: Zetta DN 

PROVIDER: S-EPMC12631473 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature