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Abstract
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Precision medicine is an emerging practice of medicine that uses a patient's specific characteristics, such as genetic information, health history, environmental exposure, and needs and preferences, to guide decisions made with regard to the prevention, diagnosis, and treatment of diseases. The challenges of data analysis in precision medicine include: heterogeneity, high-dimensionality, the need to integrate multiple data types, and the complexity of underlying biochemical mechanisms. The power and flexibility of statistical learning give it a great potential to counter these challenges and help establish good precision medicine models. In this talk, I will talk about solving some precision medicine problems using statistical learning techniques.
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