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发布者抚诺羔:文明办发布时间蔬:2019-06-27浏览次数陶私:221


主讲人陈:李敏 中南大学教授 博士生导师


时间瑞:2019年6月29日13暑互态:40


地点蜕烤:三号楼332会议厅


举办单位敲谎:数理学院


主讲人介绍伞滤建:中南大学计算机学院教授鼓、博士生导师德、副院长唾潘。CCF 生物信息学专业委员会首批委员蜕矗、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凤罚吗、ACM SIGBIO  China 秘书长信。主要从事生物信息学与数据挖掘研究分糖卵,在Bioinformatics乏、IEEE/ACM Transactions on  Computational Biology and Bioinformatics等上发表SCI期刊论文80余篇玻肛辆,论文google  scholar总引用3500余次收停,h指数29篇鞭凶,获国家授权发明专利10项截。担任ISBRA2017啃祷、ICPCSEE2017等国际会议的程序委员会主席剁箱先,是国际期刊Current  Protein & Peptide Science到拍沦、IJDMB陕、IJBRA经朴缉、Interdisciplinary Sciences:  Computational Life Sciences编委及IEEE/ACM TCBB面默蓬、Neurocomputing醛匈、Complexity眉、BMC  Bioinformatics式萝芯、BMC Genomics等的客座编委拔。  2011年被确定为湖南省青年骨干教师培养对象就,2012年获得教育部新世纪优秀人才资助假笛,主持国家自然科学基金重点项目七、优秀青年项目持褪朔、面上和青年项目各一项灌。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)刀。  


内容介绍剁殴楼: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 &nb北京赛车投注血花飞溅。“在读大三,你呢?”sp;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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