HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline.

ABSTRACT: Understanding complex diseases requires models that can integrate diverse layers of biological data while yielding insights that are biologically interpretable. Although multi-omics integration with machine learning (ML) has advanced disease prediction and biomarker discovery, most existing approaches overlook the hierarchical and regulatory relationships that connect these molecular layers. Here, we present the Hierarchical Input Neural Network (HINN), a deep learning framework that incorporates known cross-omics relationships directly into its architecture, capturing the flow of information from genomics to epigenomics, transcriptomics, and downstream biological processes. By embedding these relationships, HINN improves both predictive performance and biological interpretability. We appl

SUBMITTER: Vashishath Y 

PROVIDER: S-EPMC12458612 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature