Deep learning for protein bioinformatics and medicine


主讲人:李敏 中南大学教授 博士生导师




主讲人介绍:中南大学计算机学院教授、博士生导师、副院长。CCF 生物信息学专业委员会首批委员、中国人工智能学会-生物信息学与人工生命专业委员会常务委员、ACM SIGBIO  China 秘书长。主要从事生物信息学与数据挖掘研究,在Bioinformatics、IEEE/ACM Transactions on  Computational Biology and Bioinformatics等上发表SCI期刊论文80余篇,论文google  scholar总引用3500余次,h指数29,获国家授权发明专利10项。担任ISBRA2017、ICPCSEE2017等国际会议的程序委员会主席,是国际期刊Current  Protein & Peptide Science、IJDMB、IJBRA、Interdisciplinary Sciences:  Computational Life Sciences编委及IEEE/ACM TCBB、Neurocomputing、Complexity、BMC  Bioinformatics、BMC Genomics等的客座编委。  2011年被确定为湖南省青年骨干教师培养对象,2012年获得教育部新世纪优秀人才资助,主持国家自然科学基金重点项目、优秀青年项目、面上和青年项目各一项。获教育部高等学校科学研究优秀成果奖(自然科学奖)二等奖一项(排名第2)。  

内容介绍:Mining useful information from biomedical data is not only the crucial of life  science, but also the foundation of understanding the development of diseases.  In recent years, a lot of biomedical data have been accumulated from omics  technologies, imaging, electronic health records, and so on. Meanwhile, with the  development of big data and hardware, deep learning techniques have been  successfully used in various fields such as computer version, speech  recognition, and natural language processing. Considering their excellent  performance, we implemented some deep learning models to tackle biomedical data.  In protein bioinformatics, we focus on protein-protein interaction sites  prediction, essential protein prediction, protein function prediction, and  drug-target prediction. We built some deep learning models for extract local and  global features of protein sequences; then combined these features to improve  the predictive performance. For clinic data, we focus on electronic health  records classification and disease prediction. We developed some deep learning  models which capture the features of electronic health records and disease; then  used these features to conduct study. We hope that our studies can promote the  application of deep learning in biomedical data analysis, and provide useful  tools for solving the key problems in life science by using artificial  intelligence techniques.