Alz-QNet: A Quantum Regression Network for Studying Alzheimer's Gene Interactions

• URL: http://arxiv.org/abs/2508.04743v1
• Date: Wed, 06 Aug 2025 04:31:49 GMT
• Title: Alz-QNet: A Quantum Regression Network for Studying Alzheimer's Gene Interactions
• Authors: Debanjan Konar, Neerav Sreekumar, Richard Jiang, Vaneet Aggarwal,
• Abstract summary: Alzheimer's, being a multifactorial disease, requires understanding the gene-gene interactions underlying it for theranostics and progress.<n>Our proposed Quantum Regression Network (Alz-QNet) introduces a pioneering approach with insights from the state-of-the-art Quantum Gene Regulatory Networks (QGRN)<n>Using the proposed Alz-QNet framework, we explore the interactions between key genes ($APP$, $FGF14$, $YY1$, $EGR1$, $GAS7$, $AKT3$, $SREBF2$, and $PLD
• Score: 28.571441947616513
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Understanding the molecular-level mechanisms underpinning Alzheimer's disease (AD) by studying crucial genes associated with the disease remains a challenge. Alzheimer's, being a multifactorial disease, requires understanding the gene-gene interactions underlying it for theranostics and progress. In this article, a novel attempt has been made using a quantum regression to decode how some crucial genes in the AD Amyloid Beta Precursor Protein ($APP$), Sterol regulatory element binding transcription factor 14 ($FGF14$), Yin Yang 1 ($YY1$), and Phospholipase D Family Member 3 ($PLD3$) etc. become influenced by other prominent switching genes during disease progression, which may help in gene expression-based therapy for AD. Our proposed Quantum Regression Network (Alz-QNet) introduces a pioneering approach with insights from the state-of-the-art Quantum Gene Regulatory Networks (QGRN) to unravel the gene interactions involved in AD pathology, particularly within the Entorhinal Cortex (EC), where early pathological changes occur. Using the proposed Alz-QNet framework, we explore the interactions between key genes ($APP$, $FGF14$, $YY1$, $EGR1$, $GAS7$, $AKT3$, $SREBF2$, and $PLD3$) within the CE microenvironment of AD patients, studying genetic samples from the database $GSE138852$, all of which are believed to play a crucial role in the progression of AD. Our investigation uncovers intricate gene-gene interactions, shedding light on the potential regulatory mechanisms that underlie the pathogenesis of AD, which help us to find potential gene inhibitors or regulators for theranostics.

Related papers

• Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network [20.37811669228711]
  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.
  arXiv  Detail & Related papers  (2026-03-01T06:46:18Z)
• 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)
• 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)
• AD-GPT: Large Language Models in Alzheimer's Disease [22.79214699749541]
  Large language models (LLMs) have emerged as powerful tools for medical information retrieval.<n>But their accuracy and depth remain limited in specialized domains such as Alzheimer's disease (AD)<n>We introduce AD-GPT, a domain-specific generative pre-trained transformer designed to enhance the retrieval and analysis of AD-related genetic and neurobiological information.
  arXiv  Detail & Related papers  (2025-04-03T22:49:10Z)
• Artificial Intelligence and Deep Learning Algorithms for Epigenetic Sequence Analysis: A Review for Epigeneticists and AI Experts [2.5367788916245964]
  Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence.<n>The changes in gene regulation and expression can manifest in the form of various diseases and disorders such as cancer and congenital deformities.<n>Machine learning and artificial intelligence (AI) approaches have been extensively used for mapping epigenetic modifications to their phenotypic manifestations.
  arXiv  Detail & Related papers  (2025-04-01T01:02:34Z)
• Learning to Discover Regulatory Elements for Gene Expression Prediction [59.470991831978516]
  Seq2Exp is a Sequence to Expression network designed to discover and extract regulatory elements that drive target gene expression.<n>Our approach captures the causal relationship between epigenomic signals, DNA sequences and their associated regulatory elements.
  arXiv  Detail & Related papers  (2025-02-19T03:25:49Z)
• Unravelling Causal Genetic Biomarkers of Alzheimer's Disease via Neuron to Gene-token Backtracking in Neural Architecture: A Groundbreaking Reverse-Gene-Finder Approach [15.886985158146143]
  Alzheimer's Disease (AD) affects over 55 million people globally, yet the key genetic contributors remain poorly understood.<n>We present Reverse-Gene-Finder, a neuron-to-gene-token backtracking approach in a neural network architecture to elucidate the novel causal genetic biomarkers driving AD onset.
  arXiv  Detail & Related papers  (2025-02-06T10:24:02Z)
• Survey and Improvement Strategies for Gene Prioritization with Large Language Models [61.24568051916653]
  Large language models (LLMs) have performed well in medical exams, but their effectiveness in diagnosing rare genetic diseases has not been assessed.<n>We used multi-agent and Human Phenotype Ontology (HPO) classification to categorized patients based on phenotypes and solvability levels.<n>At baseline, GPT-4 outperformed other LLMs, achieving near 30% accuracy in ranking causal genes correctly.
  arXiv  Detail & Related papers  (2025-01-30T23:03:03Z)
• 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)
• 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)
• SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression [8.326669256957352]
  We present SimpleChrome, a deep learning model that learns the histone modification representations of genes. The features learned from the model allow us to better understand the latent effects of cross-gene interactions and direct gene regulation on the target gene expression.
  arXiv  Detail & Related papers  (2020-12-15T23:30:36Z)

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.