Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network

• URL: http://arxiv.org/abs/2603.02273v1
• Date: Sun, 01 Mar 2026 06:46:18 GMT
• Title: Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network
• Authors: Binon Teji, Subhajit Bandyopadhyay, Swarup Roy,
• Abstract summary: Prioritizing disease-associated genes is central to understanding complex disorders such as Alzheimer's disease.<n>We propose NETRA, a multimodal graph transformer framework that replaces centrality metrics with attention-driven relevance scoring.<n>A graph transformer assigns NETRA scores that quantify gene relevance in a disease-specific and context-aware manner.
• Score: 20.37811669228711
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prioritizing disease-associated genes is central to understanding the molecular mechanisms of complex disorders such as Alzheimer's disease (AD). Traditional network-based approaches rely on static centrality measures and often fail to capture cross-modal biological heterogeneity. We propose NETRA (Node Evaluation through Transformer-based Representation and Attention), a multimodal graph transformer framework that replaces heuristic centrality metrics with attention-driven relevance scoring. Using AD as a case study, gene regulatory networks are independently constructed from microarray, single-cell RNA-seq, and single-nucleus RNA-seq data. Random-walk sequences derived from these networks are used to train a BERT-based model for learning global gene embeddings, while modality-specific gene expression profiles are compressed using variational autoencoders. These representations are integrated with auxiliary biological networks, including protein-protein interactions, Gene Ontology semantic similarity, and diffusion-based gene similarity, into a unified multimodal graph. A graph transformer assigns NETRA scores that quantify gene relevance in a disease-specific and context-aware manner. Gene set enrichment analysis shows that NETRA achieves a normalized enrichment score of about 3.9 for the Alzheimer's disease pathway, substantially outperforming classical centrality measures and diffusion models. Top-ranked genes enrich multiple neurodegenerative pathways, recover a known late-onset AD susceptibility locus at chr12q13, and reveal conserved cross-disease gene modules. The framework preserves biologically realistic heavy-tailed network topology and is readily extensible to other complex disorders.

Related papers

• MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection [4.931890971425293]
  Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline and widespread dysregulation in the brain.<n>MethConvTransformer is a transformer-based deep learning framework that integrates DNA methylation profiles from both brain and peripheral tissues.
  arXiv  Detail & Related papers  (2026-01-01T00:18:33Z)
• R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression [63.97617759805451]
  Early detection of Alzheimer's disease requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility.<n>We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting.<n>R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition, subjective memory concerns, mild cognitive impairment, and AD.
  arXiv  Detail & Related papers  (2025-12-22T02:54:10Z)
• Tensor Network based Gene Regulatory Network Inference for Single-Cell Transcriptomic Data [0.0]
  This study introduces a quantum-inspired framework leveraging tensor networks (TNs) to optimally map expression data.<n>We quantify gene dependencies and establish statistical significance via permutation testing.<n>By merging quantum physics inspired techniques with computational biology, our method provides novel insights into gene regulation.
  arXiv  Detail & Related papers  (2025-09-08T17:11:12Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• Granger causal inference on DAGs identifies genomic loci regulating transcription [77.58911272503771]
  GrID-Net is a framework based on graph neural networks with lagged message passing for Granger causal inference on DAG-structured systems. Our application is the analysis of single-cell multimodal data to identify genomic loci that mediate the regulation of specific genes.
  arXiv  Detail & Related papers  (2022-10-18T21:15:10Z)
• A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine [37.442717660492384]
  We propose a novel Cross-LEvel Information Transmission network (CLEIT) framework. Inspired by domain adaptation, CLEIT first learns the latent representation of high-level domain then uses it as ground-truth embedding. We demonstrate the effectiveness and performance boost of CLEIT in predicting anti-cancer drug sensitivity from somatic mutations.
  arXiv  Detail & Related papers  (2020-10-09T22:01:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.