Multi-omic and quantum machine learning integration for lung subtypes classification

The integration of multi-omics data presents a promising frontier in cancer diagnosis and biomarker discovery, especially for complex diseases like lung cancer. However, challenges such as high dimensionality, low sample sizes, and inherent data noise hinder traditional machine-learning approaches. Quantum Machine Learning (QML) is a cutting-edge field that bridges quantum computing and machine learning to address computational challenges more effectively. This study explores the application of QML to address these limitations, offering a novel framework-Multi-Omic QML Lung Subtype Classification (MQML-LungSC)-for classifying lung cancer subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Leveraging Quantum Neural Networks with multi-dimensional feature encoding, our model efficiently integrates genomic, epigenomic, and transcriptomic data from TCGA. The model not only achieves high classification accuracy (training: 0.95; testing: 0.90) using 256 encoded features, but also demonstrates enhanced efficiency by outperforming classical machine learning methods and other quantum models with a significantly reduced architectural complexity. Notably, QNN-64,delivers performance comparable to CNN-64 while maintaining a more compact and resource-efficient design. By identifying key differentiating features, this approach advances early diagnostic capabilities and supports personalized treatment strategies. This study provides strong empirical support for the future potential of unconventional computing approaches in advancing biomedical research and applications.