Predictive biomarker discovery in cancer using a unique AI model based on set theory

Abstract

The current study applies a new artificial intelligence (AI) method, ALiX, which is based on interval arithmetic, to analyze and interpret biological data for a clinical problem: identification of biomarkers for cancer diagnosis. The key unique and important feature of this study is that ALiX provides an explanation to our clinical hypothesis in the form of a list of ranked protein biomarkers that identifies which biomarkers are the most significant drivers of the predicted outcome, a capability that is not currently available in other AI methods. Based on the significant drivers, this study identifies a machine learning model and solution for stratifying cancer patients into subtypes that will predict response to treatment.