CCF Young Computer Scientists & Engineers Forum
CCF YOCSEF 深圳
CCF YOCSEF 深圳 AC 汤步洲
CCF YOCSEF 深圳主席 卢昱明
主讲人：Dr. Stephen J. Song, Senior Research Fellow, Group Leader
单位 : Monash Biomedicine Discovery Institute、Monash University, Melbourne, Australia
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 based on statistical learning have provided efficient solutions to challenging problems of sequence classification or annotations that were previously considered difficult to address. In this talk, by combining our recent research progress, I will highlight some important developments in the prediction of two representative sequence labeling problems in computational biology, i.e. i.e. ‘target substrate labeling’ and ‘active site labeling’, based on the high-dimensional, noisy and redundant information derived from sequences and the 3D structure. I will illustrate how ML methods can extract the predictive power from a variety of features that are derived from different aspects of the data can contribute to the model performance.
Dr. Stephen J. Song is a Senior Research Fellow and Team Leader in the Biomedical Discovery Research Institute (BDI) Cancer and Infection and Immunization Program, and the Department of Biochemistry and Molecular Biology at the School of Biomedical Sciences, School of Medicine, and School of Nursing. Monash University School of Health Sciences, Melbourne, Australia. He is a bioinformatician and proficient scientist in artificial intelligence, bioinformatics, comparative genomics, cancer genomics, computational biomedical, data mining, infection and immunity, machine learning, proteomics and "biological "Medical Big Data" has a very strong professionalism, which is the highly sought-after expertise and skills in data-driven biomedical science. As one of the best performing young bioinformatics scientists in Australia, he received the NHMRC four-year biology Peter Doherty Biomedical Scholarship (2008-2012), his director ARC federation and honorary award winner Professor James Whisstock, ARC Senior Center for Excellence in Molecular Imaging, Monash University and EMBL Australian Science Director.
报告题目：Chinese Clinical Natural Language Processing: Research Status and Challenges
主讲人：Dr. Buzhou Tang, Associate Professor
单位 : School of computer science, Harbin Institute of Technology, Shenzhen (HITSZ)
This talk consists of four parts: 1) a brief introduction to clinical natural language processing (NLP), especially for Chinese clinical NLP, will be given; 2) some studies on Chinese clinical NLP tasks, including clinical entity recognization and normalization, clinical entity attribution extraction, temporal information extraction and indexing, etc., will be presented in detail; 3) challenges in Chinese clinical NLP will be discussed ; 4) status of related communities will be introduced.
Buzhou Tang is an associate professor of the school of computer science at Harbin Institute of Technology, shenzhen (HITSZ). His interests interests cover machine learning, data mining, natural language processing, signal processing and medical informatics. Professor Tang has been awarded more than 10 research funds and has published more than 80 academic papers. He has ever participated in many international challenges as team leader and won them, such as CoNLL-2010, i2b2-2012, ShARe/CLEF-2013, SemEval-2014 and CCKS-2017.