Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
Giuseppe Di, Vahagn Kirakosyan, Alexander G. Abanov, Jerome R. Busemeyer, Luca Candelori, Nadine Hartmann, Ernest T. Lam, Kharen Musaelian, Ryan Samson, Harold Steinacker, Dario Villani, Martin T. Wells, RICHARD J. WENSTRUP, Mengjia Xu
This paper introduces QCML, a quantum-inspired machine learning framework, to predict chromosomal instability from CTC morphology, achieving higher accuracy than conventional methods in high-dimensional biomedical data.
bioRxiv · May 30, 2025 · PreprintCancer Biology