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

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.

Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model

There have been several attempts to use deep learning based on brain fMRI signals to classify cognitive impairment diseases. However, deep learning is a hidden black box model that makes it difficult to interpret the process of classification. To address this issue, we propose a novel analytical framework that interprets the classification result from deep learning processes. We first derive the region of interest (ROI) functional connectivity network (FCN) by embedding functions based on their similar signal patterns. Then, using the self-attention equipped deep learning model, we classify diseases based on their FCN. Finally, in order to interpret the classification results, we employ a latent space item-response interaction network model to identify the significant functions that exhibit distinct connectivity patterns when compared to other diseases. The application of this proposed framework to the four types of cognitive impairment shows that our approach is valid for determining the significant ROI functions.