行知论坛198:Harnessing the power of machine-learning and artificial intelligence techniques to address significant biomedical classification problems in the big data-driven era

时间:2018-12-04浏览:10设置



报告时间:20181211日星期二上午10:00 - 11:30

报告地点:计算机学院4001

 

报告题目:Harnessing the power of machine-learning and artificial intelligence techniques to address significant biomedical classification problems in the big data-driven era

人:Jiangning Song, Ph.D., Monash University, Melbourne, Australia

人:於东军教授 

           

报告简介:Recent advances in high-throughput sequencing have significantly contributed to an ever-increasing gap between the number of gene products (‘proteins’) whose function is well characterised and those for which there is no functional annotation at all. Experimental techniques to determine the protein function are often expensive and time-consuming. Recently, machine-learning (ML) techniques and further artificial intelligence (AI) based on the combination of big data sets and statistical learning have provided cost-effective solutions to challenging problems of sequence classification or annotations that were previously considered difficult to address. In this talk, by combining our and other groups’ recent research progress, I will highlight some important developments in addressing two representative biomedical classification problems, i.e. i.e. ‘sequence labeling’ based on sequence data and ‘medical image classification’ based on image data. In particular, I will illustrate how ML/AI models can build up the predictive power from a variety of heterogeneous biochemical data that are derived from different aspects/properties of the data and how these can contribute to the model performance.

 

报告人简介:Dr. Song is an Associate Professor and Group Leader in the Cancer and Infection and Immunity Programs in the Biomedicine Discovery Institute (BDI), and Department of Biochemistry and Molecular Biology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia. Trained as a bioinformatician and data-savvy scientist, he has a very strong specialty in Artificial Intelligence, Bioinformatics, Comparative Genomics, Cancer Genomics, Computational Biomedicine, Data Mining, Machine Learning, Proteomics, and Biomedical Big Data, which are highly sought-after expertise and skill sets in the data-driven biomedical sciences. He was awarded a four-year NHMRC Peter Doherty Biomedical Fellowship (2008-2012) with his supervisor ARC Federation and Laureate Fellow Prof James Whisstock, Director of the ARC Centre of Excellence in Advanced Molecular Imaging, Monash University, and Scientific Head of EMBL Australia. He is an Associate Investigator of the ARC Centre of Excellence in Advanced Molecular Imaging at Monash University. He is also a member of the Monash Centre for Data Science and the Monash Bioinformatics Platform.


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