Fast polypharmacy side effect prediction using tensor factorization.

ABSTRACT:

Motivation

Adverse reactions from drug combinations are increasingly common, making their accurate prediction a crucial challenge in modern medicine. Laboratory-based identification of these reactions is insufficient due to the combinatorial nature of the problem. While many computational approaches have been proposed, tensor factorization (TF) models have shown mixed results, necessitating a thorough investigation of their capabilities when properly optimized.

Results

We demonstrate that TF models can achieve state-of-the-art performance on polypharmacy side effect prediction, with our best model (SimplE) achieving median scores of 0.978 area under receiver-operating characteristic curve, 0.971 area under precision-recall curve, and 1.000 AP@50 across 963 side effects. Nota

SUBMITTER: Lloyd O 

PROVIDER: S-EPMC11646082 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature