Translatability of Animal Models for Alzheimer's Disease Using a Machine Learning Based Workflow.

ABSTRACT: Despite significant investment, no effective disease-modifying therapies for Alzheimer's disease (AD) have been developed to date. As understanding of the underlying causes of AD evolves, numerous animal models have been generated to study the disease. However, persistent therapeutic failures raise questions about the reasons for these shortcomings, including whether they stem from poor target selection and/or limitations in replicating key aspects of AD pathophysiology in animal models. In this study, a machine learning-based workflow previously reported in the literature was modified and used to identify shared dysregulation in phenotype-defining pathways across both animal models and human datasets-termed translatable pathways. This approach provided a framework for assessing the transl

SUBMITTER: Foster-Powell A 

PROVIDER: S-EPMC12603623 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature