BACKGROUND: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. METHOD: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. RESULTS: We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. CONCLUSION: The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .
BACKGROUND: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. METHOD: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. RESULTS: We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. CONCLUSION: The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .
The discovery of biomarker sets for a targeted pathway is a challenging problem in biomedical medicine, which is computationally prohibited on classical algorithms due to the massive search space. Here, I present a quantum algorithm named QuantAnts Machine to address the task. The proposed algorithm is a quantum analog of the classical Ant Colony Optimization (ACO). We create the mixture of multi-domain from genetic networks by representation theory, enabling the search of biomarkers from the multi-modality of the human genome. Although the proposed model can be generalized, we investigate the RAS-mutational activation in this work. To the end, QuantAnts Machine discovers rarely-known biomarkers in clinical-associated domain for RAS-activation pathway, including COL5A1, COL5A2, CCT5, MTSS1 and NCAPD2. Besides, the model also suggests several therapeutic-targets such as JUP, CD9, CD34 and CD74.
This record contains the reproducibility materials for the Quantum Biomarker Discovery in Multi-Omics (QuBID-Omics) study of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) classification. It includes scripts for training-only RNA and DNA biomarker discovery, RNA–DNA feature integration, quantum neural network (QNN) and classical-baseline modeling, IBM Fez quantum-processing-unit inference, and CPTAC external validation. The release also provides ordered gene–CpG feature lists, CPTAC feature mappings, and prepared TCGA train/validation/test and CPTAC external-validation matrices for Samples 1–3. See the README for the workflow, data organization, label coding, and execution requirements.
Understanding the dynamics of T helper 1 (Th1) and T helper 2 (Th2) cells is a key challenge in designing immunotherapy and has garnered significant interest over the past decade. Although quantum machine intelligence has emerged as a novel approach to dynamic modeling, it has yet to be applied to this specific biological problem. In this study, we introduce an architecture of Quantum Neural Networks (QNNs) inspired by the biological mechanisms of T-cell differentiation. The findings show that the proposed model effectively captures Th1/Th2 dynamics and cytokine profiles across five case studies. Furthermore, additional insights from the model inference reveal T cell dominance patterns within patient groups and disease stratification. The code implementation is available at: https://github.com/namnguyen0510/Modeling-T-Helper-Cell-Dynamics-and-Cytokine-Profiles-with-Quantum-Neural-Networks.git .
Biomarkers play a central role in medicine's gradual progress toward proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, for example, for multifactorial diseases, has been challenging. The discovery of such biomarkers stands to benefit significantly from advanced information processing and means to detect complex correlations, which quantum computing offers. In this perspective, quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery. The opportunities and challenges associated with the algorithms and applications are discussed. The analysis is structured according to different data types-multidimensional, time series, and erroneous data-and covers key data modalities in healthcare-electronic health records, omics, and medical images. An outlook is provided concerning open research challenges.
Background: Quantum Neural Networks (QNNs) combine quantum computing and artificial intelligence to provide powerful solutions for high-dimensional data analysis. In magnetic resonance imaging (MRI), they address the challenges of advanced imaging sequences and data complexity, enabling faster optimization, enhanced feature extraction, and real-time clinical applications. Methods: A literature review using Scopus, PubMed, IEEE Xplore, ACM Digital Library and arXiv identified 84 studies on QNNs in MRI. After filtering for peer-reviewed original research, 20 studies were analyzed. Key parameters such as datasets, architectures, hardware, tasks, and performance metrics were summarized to highlight trends and gaps. Results: The analysis identified datasets supporting tasks like tumor classification, segmentation, and disease prediction. Architectures included hybrid models (e.g., ResNet34 with quantum circuits) and novel approaches (e.g., Quantum Chebyshev Polynomials). Hardware ranged from high-performance GPUs to quantum-specific devices. Performance varied, with accuracy up to 99.5% in some configurations but lower results for complex or limited datasets. Conclusions: The findings provide the first glimpse into the potential of QNNs in MRI, demonstrating accuracy and specificity in diagnostic tasks and biomarker detection. However, challenges such as dataset variability, limited quantum hardware access, and reliance on simulators remain. Future research should focus on scalable quantum hardware, standardized datasets, and optimized architectures to support clinical applications and precision medicine.
Quantum machine learning offers a promising new paradigm for computational biology by leveraging quantum mechanical principles to enhance cancer classification, biomarker discovery, and bioinformatics diagnostics. In this study, we apply QML to identify subtype specific biomarkers for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two predominant forms of non-small cell lung cancer. Our methodology involves a two-phase process: in Phase 1, differential expression analysis and methylation analysis between tumor and normal samples allows us to identify LUAD-specific and LUSC-specific genes, revealing potential prognostic biomarkers for cancer subtypes. Phase 2 focuses on developing a quantum classifier capable of distinguishing between LUAD and LUSC tumors, as well as between tumor and normal samples. This classifier not only enhances diagnostic precision but also demonstrates the quantum advantage in processing large-scale multiomic datasets. Our results consistently demonstrated that Sample3, representing the combined gene set, achieved the highest overall predictive performance in all metrics. These results demonstrate that QML provides an effective and scalable approach for biomarker discovery and subtype specific cancer classification. GO enrichment analysis highlighted the significant involvement of genes in synaptic signaling, ion channel regulation, and neuronal development. In the quantum phase, KEGG analysis further identified enrichment in cancer-associated pathways, including neurotrophin, MAPK, Ras, and PI3KAkt signaling, with key genes such as NGFR, NTRK2, and NTF3 suggesting a central role in neurotrophinmediated oncogenic processes. Our findings highlight the growing potential of quantum computing to advance precision oncology and next-generation biomedical analytics.