报告人：Edwin Wang 加拿大卡尔加里大学
Lots of health and genomic data have been generated, while artificial intelligence such as machine learning, especially deep learning technologies have been significantly advanced. It is a new era of precision medicine by applying machine learning and deep learning approaches into big data in medicine. I will present new concepts about how to transform genomic data into the data formats which could be applied for constructing predictive models using artificial intelligence and genomic data and lifestyle data in cancer and other diseases. The predictive models have been used for predicting cancer risk, tumor recurrence, prognosis and matching drugs for cancer patients. I will talk about several novel artificial intelligence algorithms which we have developed in past of few years in my lab.
Edwin has a undergraduate training in Computer Science and a PhD training in Molecular Genetics (UBC - University of British Columbia, 2002). After one-year postdoc training at FlyBase, a genome database of fly, he moved to National Research council Canada as a PI for establishing bioinformatics Lab for conducting computational genomics and machine learning research. In 2016, he became an AISH Chair Professor at University of Calgary. In Calgary, he has two labs – a computational lab for conducting deep learning in genomics and health informatics, and a wet lab for conducting single-cell genomics experiments for system medicine. His pioneering work of cancer network motifs has been featured in the college textbook, GENETICS (2014/2017) written by a Nobel Laureate, Dr. Hartwell and the father of systems biology, Dr. Hood. His pioneering work of microRNA of signaling networks opens the new research area: network biology of non-coding RNAs.