StackNAFLD: An Accurate Stacking Ensemble Learning Targeting NAFLD Treatment.

ABSTRACT: Nonalcoholic fatty liver disease (NAFLD) is a slow-progressing yet complex disease with multiple pathophysiological mechanisms that make it challenging to treat. In this study, we developed a machine learning (ML)-based stacking ensemble model to predict molecules that could inhibit NAFLD progression utilizing data from animal experiments. We systematically collected 75 agents from preclinical experiments and classified them as inducers and inhibitors based on each study end point. Then, we computed 12 sets of molecular fingerprints and trained them with three baseline ML models. After that, the stacked model was trained using the predictive features from the baseline models and validated with 5-fold cross-validation (5-CV) and leave-one-out cross-validation (LOOCV). We found that the stac

SUBMITTER: Intan AEK 

PROVIDER: S-EPMC12391961 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature