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Researchers within Mayo Clinic's Center for Individualized Medicine have developed an artificial intelligence (AI) platform that can uncover causal drivers and relationships embedded within complex biomedical data.
Nicholas Chia, Ph.D., John Kalantari, Ph.D., and Kia Khezeli, Ph.D., recently tested their machine-learning framework, called Causal Relation and Inference Search Platform (CRISP) on multiomic colorectal cancer samples alongside NASA Frontier Development Lab data scientists and machine-learning engineers. The Mayo team presented and published its findings at the IEEE Global Conference on Life Sciences and Technologies.
"It's like garden weeds. The dandelion keeps coming back because you don't get rid of the root. Causal inference tells you how to get rid of the root; whereas, an association study just tells you that your poor lawn health is associated with dandelions. Association doesn't tell you how to solve the problem." - Dr. Chia
"Identifying causal variables directly from observational data, and differentiating between causal relationships and misleading correlations, is a critical step toward understanding, diagnosing and treating rare and complex health conditions," says Dr. Kalantari, a machine-learning scientist within the center's Microbiome program. "No one, to our knowledge, has developed or applied such causal and invariant approaches for multiomic biomedical data before." Dr. Kalantari is the principal investigator of the study.
Dr. Kalantari says the novelty of such a platform comes from its ability to discover the underlying cause-and-effect relationships driving a patient's disease progression.
"By leveraging all available multiomic and clinical data types, the platform's algorithms can be used to reveal the hidden causes of a disease in order to identify new therapeutic targets and mechanisms for disease prevention," Dr. Kalantari explains.
Read the rest of the article on the Center for Individualized Medicine blog.
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