A quantum mechanics approach to artificial intelligence can improve cancer outcomes

Researchers led by Orly Alter from the University of Utah have developed a novel quantum mechanics-based technique using artificial intelligence and machine learning (AI/ML) capable of analyzing millions of tumor and blood DNA and RNA features from a small number (71) of neuroblastoma patient samples.
This AI technique, grounded in mathematical frameworks from quantum mechanics, focuses on understanding the disease mechanism by incorporating vast molecular data and deriving interpretable predictors, which outperform standard biomarkers in assessing patient life expectancy in response to treatment.
The technique introduces specific interpretable predictors of life expectancy that are experimentally validated across different groups of children and thus applicable to more general populations.
Unlike conventional AI approaches, it is well-suited for small clinical trial datasets by handling noisy, high-dimensional multiomic data and suggests potential for precision medicine development.
The algorithms have wider applications beyond medicine, including potential areas such as sustainable energy.
The research was funded by various grants including from the NIH and NSF, and published in the journal Applied Physics Letters (APL) Quantum on June 10, 2024.

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