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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alz-QNet: A quantum regression network for studying Alzheimer's gene interactions.

Debanjan Konar1, Neerav Sreekumar2, Richard Jiang3

  • 1Purdue University, West Lafayette, IN, USA.

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|August 6, 2025
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Summary

This study introduces Alz-QNet, a quantum regression network, to uncover gene interactions in Alzheimer's Disease (AD). Understanding these genetic links offers new pathways for gene expression-based therapies and theranostics in AD.

Keywords:
Alzheimer’s diseaseComputational biologyGene regulatory networksQuantum machine learning

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Area of Science:

  • Genetics
  • Computational Biology
  • Neuroscience

Background:

  • Alzheimer's Disease (AD) is a complex, multifactorial neurodegenerative disorder.
  • Understanding molecular mechanisms, especially gene-gene interactions, is crucial for AD theranostics.
  • Current research faces challenges in elucidating these intricate genetic networks.

Purpose of the Study:

  • To decode how key Alzheimer's Disease genes are influenced by other genes during disease progression.
  • To introduce a novel Quantum Regression Network for Alzheimer's Disease (Alz-QNet) for analyzing gene interactions.
  • To explore potential gene expression-based therapies for AD.

Main Methods:

  • Utilized a novel Quantum Regression Network (Alz-QNet) inspired by Quantum Gene Regulatory Networks (QGRNs).
  • Analyzed gene interactions within the Entorhinal Cortex (EC) microenvironment of AD patients.
  • Studied genetic samples from the GSE138852 database, focusing on genes like APP, FGF14, YY1, and PLD3.

Main Results:

  • Uncovered intricate gene-gene interactions crucial to AD pathogenesis.
  • Identified potential regulatory mechanisms underlying Alzheimer's Disease progression.
  • Provided insights into how specific genes influence each other in the AD context.

Conclusions:

  • The Alz-QNet framework offers a pioneering approach to unraveling AD's genetic underpinnings.
  • Findings shed light on potential gene inhibitors or regulators for AD theranostics.
  • This research paves the way for advanced gene expression-based therapeutic strategies for Alzheimer's Disease.