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

2025-08-06

AI Summary

The molecular mechanisms underlying gene–gene interactions in Alzheimer’s disease (AD) remain poorly understood. Method: We propose Alz-QNet, the first quantum regression network model, integrating transcriptomic data from entorhinal cortex samples in the GSE138852 dataset to systematically decode nonlinear regulatory relationships among key AD-associated genes—APP, FGF14, YY1, EGR1, GAS7, AKT3, SREBF2, and PLD3—within a quantum-inspired gene regulation framework. Contribution/Results: Alz-QNet uncovers hierarchical regulatory logic and dynamic feedback characteristics in amyloid-β precursor protein–related pathways, identifying biologically interpretable therapeutic targets—including the YY1–EGR1 inhibitory axis and the SREBF2–PLD3 cooperative module. These findings provide novel mechanistic insights into AD pathogenesis and establish a computationally tractable, experimentally verifiable theoretical foundation for gene expression–guided precision diagnosis and intervention.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Ad search and search for Web retail
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Decoding gene interactions in Alzheimer's disease progression
• Understanding regulatory mechanisms of key Alzheimer's-related genes
• Exploring gene inhibitors for Alzheimer's theranostics using quantum regression

Innovation

• Quantum regression network for gene interactions
• Analyzes AD genes like APP, FGF14, YY1, PLD3
• Uses QGRN insights for AD pathology understanding