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发布者顾浑:文明办作者技:发布时间卧:2019-06-27浏览次数胃:795


主讲人违类授:李敏 中南大学教授 博士生导师


时间福:2019年6月29日13侥:40


地点藕病:三号楼332会议厅


举办单位驰讲:数理学院


主讲人介绍肪勘裤:中南大学计算机学院教授冈僵、博士生导师令克、副院长氖颇死。CCF 生物信息学专业委员会首批委员犊捞捎、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翱搬、ACM SIGBIO  China 秘书长庞慧肉。主要从事生物信息学与数据挖掘研究惦呸扮,在Bioinformatics艇赊钢、IEEE/ACM Transactions on  Computational Biology and Bioinformatics等上发表SCI期刊论文80余篇盲,论文google  scholar总引用3500余次饱,h指数29鲍,获国家授权发明专利10项铺。担任ISBRA2017媳般俯、ICPCSEE2017等国际会议的程序委员会主席焚魏,是国际期刊Current  Protein & Peptide Science枢聪、IJDMB钞亭井、IJBRA垃舷、Interdisciplinary Scienc北京赛车投注M e t a t r o n“就是在找茬1es:  Computational Life Sciences编委及IEEE/ACM TCBB闷淘、Neurocomputing顾浚邪、Complexity钎莫醚、BMC  Bioinformatics柯胺殊、BMC Genomics等的客座编委糖臣。  2011年被确定为湖南省青年骨干教师培养对象斑飞唾,2012年获得教育部新世纪优秀人才资助搓,主持国家自然科学基金重点项目歌、优秀青年项目父免、面上和青年项目各一项韧鄙级。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)匈邢。  


内容介绍梅莆: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.

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