Anthoula Lazaris , Migmar Tsamchoe , Susan Kaplan , Peter Metrakos , Nathan Hayes
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Predictive biomarker discovery in cancer using a unique AI model based on set theory
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.
期刊介绍：
Informatics in Medicine Unlocked (IMU) is an international gold open access journal covering a broad spectrum of topics within medical informatics, including (but not limited to) papers focusing on imaging, pathology, teledermatology, public health, ophthalmological, nursing and translational medicine informatics. The full papers that are published in the journal are accessible to all who visit the website.
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