Quantum mechanics-based AI improves cancer prognoses in small patient groups
Salt Lake City (LabNews Media LLC) – A new AI method based on quantum mechanics principles can derive more precise prognoses and therapy recommendations for cancer patients from complex molecular data, even in small patient groups. This is shown by a study from the University of Utah published in the journal APL Quantum. The team led by Orly Alter developed algorithms based on the quantum mechanical concepts of superposition and entanglement. These so-called “multitensor comparative spectral decompositions” break down multiple layers of molecular data – including tumor and blood DNA as well as tumor RNA – into coherent patterns. When applied to neuroblastoma data from 71 patients, the researchers were able to derive new predictors of children's life expectancy that surpassed conventional biomarkers. The predictions were successfully validated in independent patient groups. Furthermore, they provided interpretable insights into disease mechanisms and potential targets for new therapies. "It's about much more than just a…
