Leveraging Hamiltonian neural flow for robust single-cell multi-omics integration: application to Alzheimer's disease.

Overview

1. College of Information Engineering, Shanghai Maritime University, Shanghai, China

Abstract

Alzheimer’s disease (AD) progression involves complex molecular interactions across multiple biological layers, yet integrating high-dimensional single-cell multi-omics data remains computationally challenging. While Graph Convolutional Networks (GCNs) effectively model cell-gene interaction topologies, they face three critical limitations: over-smoothing in deep architectures, instability under data perturbations, and lack of mechanistic interpretability—obstacles that impede clinical translation. The Hamiltonian Graph Convolutional Network (HGCN), a physics-inspired framework integrating symplectic dynamics with graph-based learning, is proposed in this study, which incorporates energy-conserving Hamiltonian mechanics to address these limitations through: (1) geometric constraints that prevent over-smoothing, (2) stable gradient propagation via symplectic integration, and (3) interpretable phase space representations of cellular states. To validate the effectiveness of the HGCN model, it was evaluated on three single-cell multi-omics datasets: an AD prefrontal cortex dataset, and peripheral blood benchmarks. Meanwhile, differential analysis emerged as the most effective feature extraction strategy in the evaluated experimental setting through systematic preprocessing comparisons. On the AD composite classification task requiring simultaneous prediction of cell type and disease state, HGCN achieved 92.28% accuracy and 0.9228 F1-score, significantly outperforming baseline GCN (88.59% accuracy, 0.8860 F1-score). Phase space visualization revealed biologically meaningful patterns: Inhibitory neurons exhibited heterogeneous subtype structures, while disease states showed symmetric geometric organization suggesting cell-type-invariant pathological mechanisms. Robustness experiments on citation networks demonstrated superior resilience to both feature and structural perturbations compared to standard GCN, with performance advantages increasing under higher perturbation intensities. These results establish HGCN as a robust, interpretable framework for multi-omics integration in complex disease analysis, with potential applications in precision medicine.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• geo:GSE214979, at NCBI GEO; found in “Data availability statement”

Data availability statement

The original contributions presented in the study are publicly available. The datasets can be found at the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu), the 10x Genomics website (https://www.10xgenomics.com/resources/datasets), and the Gene Expression Omnibus (GEO) under accession number GSE214979 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi>acc=GSE214979).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 5 keywords, 38 references.

Cite

This paper

Huang, Z., Kong, W., & Wang, S. (2026). Leveraging Hamiltonian neural flow for robust single-cell multi-omics integration: application to Alzheimer's disease. Frontiers in genetics, 17, 1795752. https://doi.org/10.3389/fgene.2026.1795752

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1093/bioinformatics/btag652 [code]

mmVelo: a deep generative model for estimating cell state-dependent dynamics across multiple modalities.

Journal: Bioinformatics (Oxford, England)

In common: genetics / omics, 4 references

[2] doi:10.1128/mbio.01294-26

Single-cell analysis of Powassan virus-infected brains reveals age-dependent neuroinflammatory crosstalk and progressive Alzheimer's-like APP/Aβ accumulation.

Journal: mBio

In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 4 references

[3] doi:10.1002/alz.71804

A meta-refined human Alzheimer's disease-associated gene subset shows partial mouse-model pathway correspondence and limited cross-cohort machine-learning transportability.

Journal: Alzheimer's & dementia : the journal of the Alzheimer's Association

In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 3 references

[4] doi:10.1038/s41467-026-74171-0 [code]

Cluster replicability in single-cell and single-nucleus atlases of the mouse brain.

Journal: Nature communications

In common: genetics / omics, cellular / molecular, 3 references

[5] doi:10.1038/s41597-026-07173-8 [code]

The Cell Ontology in the age of single-cell omics.

Journal: Scientific data

In common: genetics / omics, cellular / molecular, 3 references

[6] doi:10.1038/s41586-026-10793-0

Cell-type signatures of Alzheimer's disease shared across population groups.

Journal: Nature

In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 3 references

[7] doi:10.3390/biomedicines14050998 [code]

Integrative Multi-Omics and Machine Learning Analysis Identifies Therapeutic Targets and Drug Repurposing Candidates for Alzheimer's Disease.

Journal: Biomedicines

In common: Alzheimer's / dementia, genetics / omics, cellular / molecular, 3 references

[8] doi:10.1093/bioinformatics/btag253 [code]

PEARL: integrative multi-omics classification and omics feature discovery via deep graph learning.

Journal: Bioinformatics (Oxford, England)

In common: Alzheimer's / dementia, genetics / omics, 2 references

[9] doi:10.1038/s41593-026-02367-0 [code]

A reproducible three-dimensional model of human brain tissue to investigate physiological and disease-associated microglia phenotypes.

Journal: Nature neuroscience

In common: Alzheimer's / dementia, cellular / molecular, 3 references

[10] doi:10.1038/s41586-026-10679-1 [code]

Cortical development dynamics across autism spectrum disorder mouse models.

Journal: Nature

In common: genetics / omics, cellular / molecular, 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.