邮箱登录 | 所务办公 | 收藏本站 | English | 中国科学院
 
首页 计算所概况 新闻动态 科研成果 研究队伍 国际交流 技术转移 研究生教育 学术出版物 党群园地 科学传播 信息公开
国际交流
学术活动
交流动态
现在位置:首页 > 国际交流 > 学术活动
Novel computational methods towards understanding nucleic acid – protein interactions
2019-07-18 | 【 【打印】【关闭】

  Abstract:

  Biological molecules perform their functions through interaction with other molecules. Nucleic acid (DNA and RNA) – protein interaction is behind the majority of biological processes, such as DNA replication, transcription, post-transcription regulation, and translation. In this talk, I will introduce our work on developing two novel computational methods towards understanding nucleic acid – protein interactions. The first one is a structural alignment method, PROSTA-inter, that automatically determines and aligns interaction interfaces between two arbitrary types of complex structures to detect their structural similarity. The second one is a deep learning-based computational framework, NucleicNet, that predicts the binding specificity of different RNA constituents on the protein surface, based only on the structural information of the protein.

  Bio:

  Dr. Xin Gao is an associate professor of computer science in the Computer, Electrical and Mathematical Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He is also a PI in the Computational Bioscience Research Center at KAUST and an adjunct faculty member at David R. Cheriton School of Computer Science at University of Waterloo, Canada. Prior to joining KAUST, he was a Lane Fellow at Lane Center for Computational Biology in School of Computer Science at Carnegie Mellon University, U.S.. He earned his bachelor degree in Computer Science in 2004 from Computer Science and Technology Department at Tsinghua University, China, and his Ph.D. degree in Computer Science in 2009 from David R. Cheriton School of Computer Science at University of Waterloo, Canada.

  Dr. Gao’s research interest lies at the intersection between computer science and biology. In the field of computer science, he is interested in developing machine learning theories and methodologies. In the field of bioinformatics, he group works on building computational models, developing machine learning techniques, and designing efficient and effective algorithms, to tackle key open problems along the path from biological sequence analysis, to 3D structure determination, to function annotation, and to understanding and controlling molecular behaviors in complex biological networks. He has co-authored more than 170 research articles in the fields of bioinformatics and machine learning.

 
网站地图 | 联系我们 | 意见反馈 | js13.com
 
京ICP备05002829号 京公网安备1101080060号
531msc.com 金博士城下载 rfd79.com 申博太阳城用户注册平台 bet36注册最高返水
大众棋牌官网注册 新葡京游戏电子优惠送不停 澳门金沙官网最高返水 乐百家真人游戏最高占成 12博开户
博彩网上投注最高占成 bwin亚洲会员网最高占成 华逸娱乐vip开户 希尔顿城官网 梦之城官网桌面下载最高占成
msc975.com 皇家真人赌大小 申博官网开户登入 澳门足彩网站官网 必發游戏官网