Can a Quantum Support Vector Machine algorithm be utilized to identify Key Biomarkers from Multi-Omics data of COVID19 patients?

2025-04-29

AI Summary

This study investigates the feasibility of quantum support vector machines (QSVMs) for identifying multi-omics biomarkers in COVID-19. To address proteomic and metabolomic data, we propose a QSVM framework integrating ridge regression-based feature ranking with multiple quantum kernels—namely, amplitude encoding, angle encoding, ZZ feature mapping, and projection quantum kernels—and systematically compare its performance against classical SVM (CSVM). This work presents the first systematic evaluation of diverse quantum kernels on a real-world biomedical multi-omics classification task. Numerical simulations demonstrate that all QSVM variants achieve classification accuracy comparable to or exceeding that of CSVM. Crucially, QSVM decision boundaries preserve the biomarker importance ranking derived from ridge regression, confirming simultaneous high predictive performance and model interpretability. Our results provide empirical validation and a methodological framework for leveraging quantum machine learning in precision biomarker discovery.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Multi-modal Vision
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Identifying key biomarkers for COVID-19 from high-dimensional multi-omics data is critical for advancing both diagnostic and pathogenesis research. In this study, we evaluated the applicability of the Quantum Support Vector Machine (QSVM) algorithm for biomarker-based classification of COVID-19. Proteomic and metabolomic biomarkers from two independent datasets were ranked by importance using ridge regression and grouped accordingly. The top- and bottom-ranked biomarker sets were then used to train and evaluate both classical SVM (CSVM) and QSVM models, serving as predictive and negative control inputs, respectively. The QSVM was implemented with multiple quantum kernels, including amplitude encoding, angle encoding, the ZZ feature map, and the projected quantum kernel. Across various experimental settings, QSVM consistently achieved classification performance that was comparable to or exceeded that of CSVM, while reflecting the importance rankings by ridge regression. Although the experiments were conducted in numerical simulation, our findings highlight the potential of QSVM as a promising approach for multi-omics data analysis in biomedical research.

Problem

• Identifying COVID-19 biomarkers from multi-omics data
• Evaluating Quantum SVM for biomarker classification
• Comparing QSVM and classical SVM performance

Innovation

• QSVM algorithm for COVID-19 biomarker classification
• Multiple quantum kernels enhance classification performance
• Ridge regression ranks biomarkers for QSVM input