Predicting DDI-induced pregnancy and neonatal ADRs using sparse PCA and stacking ensemble approach.

ABSTRACT: Predicting Drug-Drug interaction (DDI)-induced adverse drug reactions (ADRs) using computational methods is challenging due to the availability of limited data samples, data sparsity, and high dimensionality. The issue of class imbalance further increases the intricacy of prediction. Different computational techniques have been presented for predicting DDI-induced ADRs in the general population; however, the area of DDI-induced pregnancy and neonatal ADRs has been underexplored. In the present work, a sparse ensemble-based computational approach is proposed that leverages SMILES strings as features, addresses high-dimensional and sparse data using Sparse Principal Component Analysis (SPCA), mitigates class imbalance with the Multilabel Synthetic Minority Oversampling Technique (MLSMOTE), a

SUBMITTER: Chaurasia A 

PROVIDER: S-EPMC12569586 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature