报告题目：Novel computational methods towards understanding nucleic acid – protein interactions

报告人：高欣 教授（阿卜杜拉国王科技大学）

主持人：汪国华 教授

主办单位：林木遗传育种国家重点实验室，信息与计算机工程学院

报告时间：2020年1月10日（周五），10:00--11:00

地点：科技楼主310学术报告厅

报告内容：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 first give a brief introduction about the research in Structural and Functional Bioinformatics Group (http://sfb.kaust.edu.sa). I will then 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.

报告人简介：Dr. Xin Gao is an associate professor of computer science at King Abdullah University of Science and Technology (KAUST), Saudi Arabia. He is also the Acting Associate Director of 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, to understanding and controlling molecular behaviors in complex biological networks, and, recently, to biomedicine and healthcare. He has co-authored more than 190 research articles in the fields of bioinformatics and machine learning.

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