Breast Cancer Biomarker Discovery Using an Enhanced Quantum-Based Avian Navigation Optimizer and Ensemble Learning Model

Breast cancer remains a global health challenge, and early detection is crucial for improving survival rates. However, traditional biomarker detection methods in machine learning face challenges such as high false positives, large gene datasets, and limited sample sizes. These challenges are exacerbated by the limitations of traditional differential evolution techniques, which struggle with scalability and effectiveness in high-dimensional, complex problems. This study introduces Ensemble-Based Logical Binary QANA (LBQANA_En), an improved differential evolution variant tailored for large-scale global optimization in gene expression analysis. By integrating six gene expression datasets, LBQANA_En overcomes the constraints of small sample sizes and the complexities inherent in gene expression data. Inspired by the quantum navigation of migratory birds and leveraging logical operators like XOR and OR, LBQANA_En outperforms other binary QANA variants in biomarker detection. The algorithm successfully identifies key biomarkers, including LPL, LEP, CD36, CDC20, TOP2A, and EZH2, significantly improving breast cancer detection accuracy and reducing false positives, achieving an impressive F1 score of 98.958%. These biomarkers provide critical insights into important pathways, such as AMPK and PPAR signaling, setting a new benchmark in computational biology and bioinformatics research, and paving the way for advancements in diagnostic techniques and medical science.