Quantum variational graph-driven neural framework for genomic-clinical integration in precision diagnosis.

Area of Science:

• Biomedical informatics
• Quantum computing applications
• Machine learning in healthcare

Background:

• Integrating multimodal biomedical data (genomic, clinical) for precision diagnosis is challenging due to data complexity and heterogeneity.
• Existing machine learning and quantum-classical models lack interpretability and scalability for complex cross-modal data integration.
• High dimensionality and non-linear dependencies hinder effective analysis of integrated patient data.

Purpose of the Study:

• To propose a Quantum Variational Graph-Driven Neural (QVGDN) framework for advanced precision diagnosis.
• To address limitations in integrating multimodal biomedical data using quantum-enhanced computing.
• To capture complex cross-modal interactions and relational patient structures effectively.

Main Methods:

• QVGDN framework utilizes quantum adaptive interference for modality-specific feature extraction.
• Integrates genomic and clinical features via variational encoding and cross-factorized attention fusion.
• Employs a multi-omic quantum entangled kernel-based external graph neural network for relational diagnosis.
• Hyperparameter tuning performed using the Superb Fairy-wren Optimization Algorithm.

Main Results:

• QVGDN achieved 99.12% accuracy on benchmark datasets.
• Demonstrated high performance in noisy and limited data conditions.
• Significantly improved diagnostic precision, sample efficiency, and relational interpretability compared to baseline models.

Conclusions:

• The QVGDN framework offers a scalable solution for real-time precision diagnostics.
• Highly suitable for clinical decision support systems requiring interpretability and computational efficiency.
• Represents a significant advancement in leveraging quantum computing for biomedical data analysis.