Cited by 10Bioinformatics and Genomic NetworksAlzheimer's disease research and treatmentsComputational Drug Discovery Methods

Network-based analysis of Alzheimer’s Disease genes using multi-omics network integration with graph diffusion

Sebastian, Softya, Roy, Swarup, Kalita, Jugal·2025·Journal of Biomedical Informatics

Abstract

Alzheimer's Disease (AD) is a complex neurodegenerative disorder affecting millions worldwide. Despite extensive research, the mechanisms behind AD remain elusive. Many studies suggest that disease-responsible genes often act as hub genes in biological networks. However, this assumption requires further investigation in the context of AD. To examine the network characteristics of known AD genes, it is crucial to construct a highly confident network, which is challenging to achieve using a single data source. This work integrates multi-omics networks inferred from microarray, single-cell RNA sequencing, and single-nuclei RNA sequencing expression data, weighted with protein interaction and gene ontology information. We generate a high-quality integrated network by utilizing various inference methods and combining them through a graph diffusion-based integration approach. This network is then analyzed to investigate the properties of known AD-specific genes. Our findings reveal that AD genes are not always high-degree or central hub nodes in the network. Instead, these genes are distributed across different quartiles of degree centrality while maintaining significant interconnections for effective regulation. Furthermore, our study highlights that peripheral genes, often overlooked, also play crucial roles by connecting to relevant disease nodes and hub genes. These findings challenge the conventional understanding that AD-responsible genes are primarily the hub genes in the network, offering new insights into the complex regulatory mechanisms of AD and suggesting novel directions for future research.

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Crossref · 10.1016/j.jbi.2025.104797

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  • AI-driven multi-omics approaches have shown considerable promise across key domains, including early diagnosis, mechanistic elucidation, biomarker discovery, therapeutic development, and patient stratification.

    “These integrative approaches have shown considerable promise across key domains, including early diagnosis, mechanistic elucidation, biomarker discovery, therapeutic development, and patient stratification (Fig. 4, Table 325,53,58,226,237,251,253,255,261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 276,278,283,287,289,319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330,”

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Authors
Sebastian, S., Roy, S., & Kalita, J.
Affiliations
  • Vellore Institute of Technology University (IN)
  • Sikkim University (IN)
  • University of Colorado Colorado Springs (US)
Corresponding author
Roy, Swarup
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2025-04
Volume
164
Pages
104797
ISSN
1532-0464 (ISSN-L)
OpenAlex
W4407852213
Cited by
10
References
38
Citations by year
2026: 4 · 2025: 6
FWCI
3.02
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
Alzheimer's disease, gene regulatory networks, hub genes, network integration, graph diffusion, multi-omics integration, single-cell RNA sequencing, single-nucleus RNA sequencing, gene expression microarrays, protein-protein interaction, Gene Ontology, degree centrality
MeSH terms
Multiomics, Algorithms, Alzheimer Disease, Humans, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Genomics, Databases, Genetic, Gene Regulatory Networks, Protein Interaction Maps, Gene Ontology

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