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Machine Learning on Disease Prediction (Eric Zhang et al.)
A key public health challenge is to identify individuals at high-risk for common diseases in order to enable prescreening or preventive therapies. Much effort has been made in identifying disease causal genomic variants and evaluating their contribution in disease prediction. Unlike single gene diseases that are usually caused by inherited monogenic mutations, common diseases have multifactorial etiologies that involve the interplay of both genetic and non-genetic factors. Therefore, how to effectively identify high-risk incident cases from "multi-level" information are core goals for precision medicine.
In this project, we plan to develop machine learning (especially deep learning) algorithms on integrating genomic data, clinical images, clinical records and lifestyle information to predict human complex diseases. You will get hands-on training for statistical modelling and learn how to apply them to resolve real word problem. Also you can observe how can the big data change the healthcare in the near future.
Related Publications:
- James Zou, Mikael Huss, Abubakar Abid, Pejman Mohammadi, Ali Torkamani and Amalio Telenti. A primer on deep learning in genomics. Nature genetics 51, 12-18(2019)
- Jianxing He, Sally L. Baxter, Jie Xu, Jiming Xu, Xingtao Zhou and Kang Zhang. The practical implementation of artificial intelligence technologies in medicine. Nature Medicine 25, 30-36 (2019)
For further information on this research topic, please contact Dr. Eric Zhang.
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