HINN prioritizes multi-omics biomarkers associated with cognitive decline.
5 affiliations
- 1Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, United States.
- 2Center for Computational Life Sciences, University of North Texas, Denton, TX 76203, United States.
- 3BioDiscovery Institute, University of North Texas, Denton, TX 76203, United States.
- 4School of Computing and Information Sciences, Florida International University, Miami, FL 33199, United States.
- 5Department of Mathematics, University of North Texas, Denton, TX 76203, United States.
Abstract
Motivation
Integrating multi-omics data for disease prediction remains challenging due to the complexity of cross-layer biological interactions and the limited use of prior biological knowledge in many machine learning models. We present the Hierarchical Input Neural Network (HINN), a deep learning framework that incorporates biologically informed connections across genomic, epigenetic, and transcriptomic layers to model regulatory relationships in a structured and interpretable manner. HINN was applied to blood-derived multi-omics data to predict cognitive assessment scores, including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment, Alzheimer's Disease Assessment Scale-Cognitive Subscale 11 tasks (ADAS11), and Rey Auditory Verbal Learning Test. A biologically guided feature construction pipeline was used to integrate genome-wide association studies-derived single nucleotide polymorphisms (SNPs), promoter-region DNA methylation, and gene expression data through Gene Ontology Biological Process and KEGG pathway mappings.Results
The model was evaluated using a rigorous training strategy that separates feature selection from testing and incorporates cross-validation as well as repeated runs to ensure robustness. Across all cognitive outcomes, HINN achieved the lowest mean normalized mean squared error among the evaluated models, with statistically significant improvements over most baseline methods. Ablation analyses showed that the contributions of individual omics modalities and biologically informed connectivity varied across cognitive outcomes, with the clearest benefits of structured connectivity observed for ADAS11 and MMSE. Feature attribution analysis identified a multi-omics cascade involving SNP (rs116557230), CpG site (cg24041822), and a SOCS4 gene expression probe, which showed consistent associations with multiple cognitive outcomes. These findings highlight the ability of HINN to uncover biologically meaningful cross-omics relationships relevant to cognitive decline. Together, this work demonstrates that integrating deep learning with biological knowledge enables biomarker discovery for complex diseases.Availability and implementation
All code, sample data, and instructions are publicly available via GitHub (https://github.com/bozdaglab/HINN) and archived on Zenodo (https://doi.org/10.5281/zenodo.19197057) to ensure reproducibility and long-term accessibility.
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BibTeX
@article{vashishath2026hinn,
title = {{HINN prioritizes multi-omics biomarkers associated with cognitive decline.}},
author = {Yashu Vashishath and Sarah Anne Beaver and Fahad Saeed and Serdar Bozdag and Alzheimer’s Disease Neuroimaging Initiative},
journal = {Bioinformatics (Oxford, England)},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag741},
url = {https://europepmc.org/article/MED/42842388},
note = {CC BY},
}
RIS
TY - JOUR
TI - HINN prioritizes multi-omics biomarkers associated with cognitive decline.
AU - Yashu Vashishath
AU - Sarah Anne Beaver
AU - Fahad Saeed
AU - Serdar Bozdag
AU - Alzheimer’s Disease Neuroimaging Initiative
PY - 2026
DA - 2026/10/01
JO - Bioinformatics (Oxford, England)
DO - 10.1093/bioinformatics/btag741
UR - https://europepmc.org/article/MED/42842388
AB - Motivation Integrating multi-omics data for disease prediction remains challenging due to the complexity of cross-layer biological interactions and the limited use of prior biological knowledge in many machine learning models. We present the Hierarchical Input Neural Network (HINN), a deep learning framework that incorporates biologically informed connections across genomic, epigenetic, and transcriptomic layers to model regulatory relationships in a structured and interpretable manner. HINN was applied to blood-derived multi-omics data to predict cognitive assessment scores, including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment, Alzheimer's Disease Assessment Scale-Cognitive Subscale 11 tasks (ADAS11), and Rey Auditory Verbal Learning Test. A biologically guided feature construction pipeline was used to integrate genome-wide association studies-derived single nucleotide polymorphisms (SNPs), promoter-region DNA methylation, and gene expression data through Gene Ontology Biological Process and KEGG pathway mappings.Results The model was evaluated using a rigorous training strategy that separates feature selection from testing and incorporates cross-validation as well as repeated runs to ensure robustness. Across all cognitive outcomes, HINN achieved the lowest mean normalized mean squared error among the evaluated models, with statistically significant improvements over most baseline methods. Ablation analyses showed that the contributions of individual omics modalities and biologically informed connectivity varied across cognitive outcomes, with the clearest benefits of structured connectivity observed for ADAS11 and MMSE. Feature attribution analysis identified a multi-omics cascade involving SNP (rs116557230), CpG site (cg24041822), and a SOCS4 gene expression probe, which showed consistent associations with multiple cognitive outcomes. These findings highlight the ability of HINN to uncover biologically meaningful cross-omics relationships relevant to cognitive decline. Together, this work demonstrates that integrating deep learning with biological knowledge enables biomarker discovery for complex diseases.Availability and implementation All code, sample data, and instructions are publicly available via GitHub (https://github.com/bozdaglab/HINN) and archived on Zenodo (https://doi.org/10.5281/zenodo.19197057) to ensure reproducibility and long-term accessibility.
SN - 1367-4803
SN - 1367-4811
ER -