Novel computational methods towards understanding nucleic acid – protein interactions

  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.

附件:
拉斯维加斯游戏在线 41彩票论坛 t6女优BBIN馆 皇冠官方网址注册 金沙会无锡消费登入
海立方最佳选择 中国足球队之歌 宝马上线 澳门网上赌场周周领取工资 飞禽老虎机玩法技巧
博天下棋牌4大优惠 og东方厅维护 新浪足球解说员 亚洲博彩公司 申博在线官网开户登入
名士亚洲娱乐 太阳城真人电子娱乐 太阳城申博app客户端下载 葡京信誉赌场 大都会欢乐棋牌