Title: A preliminary study on the learning mechanism of seabet app download
Reporting location：Computer seabet app download313Lecture Hall，7month1Afternoon2:30-4:00
Abstract: In recent years，Researchers are beginning to apply neural networks to graph data，Spurred on the seabet app download boom of graph neural networks。Most graph neural networks are equivalent to low-pass filters，By aggregating the characteristic information of neighbor nodes along the network structure，Realizing effective aggregation of network structure and attribute information。This report will explore the key operations of graph neural networks (such as aggregation mechanisms、Message propagation mechanism、The role of low-pass filtering，Propose some novel graph neural network models。
Personal introduction:Ishikawa Professor of School of Computer seabet app download, Beijing University of Posts and Telecommunications、PhD supervisor、Deputy Director of Beijing Key Laboratory of Intelligent Communication Software and Multimedia。Main research direction: data mining、Machine Learning、Artificial intelligence and big data analysis。Published more than 60 papers as the first author or corresponding author in CCF A/B journals and conferences in the past 5 years，An English monograph，Apply for more than 30 patents，Relevant research results applied to Alibaba、Tencent、Huawei、Meituan and other companies。Won the ADMA2011/AMDA2018 International Conference Best Paper Award。Research results won 5 provincial and ministerial awards，Including the second prize of Beijing/CCF seabet app download and Technology Award in Natural seabet app download (ranked first) and the first prize of Wu Wenjun Artificial Intelligence seabet app download and Technology Progress Award (ranked third)，Won the titles of Young Talents and Pioneer of Teacher Ethics in Beijing Higher Education Institutions。
Title:Based onseabet app downloadseabet app download on drug repositioning prediction technology
Report time：Computer seabet app download313Lecture Hall，2021Year7month1No.16:00-17:00
Abstract:Traditional drug seabet app download and development has a long life cycle、Huge cost，Problems with higher risks，Resulting in lower success rate of new drug development。Drug repositioning refers to predicting new indications for existing drugs through various technologies，Can be used to shorten the drug development process，And reduce R&D costs and risks。Graph neural network is one of the hottest seabet app download directions in machine learning，Has been successfully applied to image recognition、Natural Language Processing、Recommendation systems and many other fields。Since drug molecules can be naturally represented in the form of graphs，Spurred on the seabet app download boom of graph neural networks in bioinformatics。This report will introduce the seabet app download on prediction technology for multiple tasks in drug repositioning based on graph neural network，Thus providing potential clues for large-scale experimental screening，Further reduce costs，Shorten the R&D cycle。
Personal introduction:Lin Xuan Lecturer at the School of Computer seabet app download, Xiangtan University.2021Graduated with a Ph.D. from the School of Information seabet app download and Engineering, Hunan University.2019Year to Year2020Year，Visiting as a jointly trained doctoral student at the seabet app download of Illinois at Chicago。The main research direction is graph neural network and bioinformatics。Molecular property prediction in drug discovery、Compound-target interaction prediction、Has in-depth research on graph representation learning and other aspects，And achieved a series of research results。In artificial intelligence、Published papers in top international or mainstream journal conferences in the fields of bioinformatics and other fieldsMore than 10 articles，includes AAAI、IJCAI、ECAI、BIBM、Briefings in Bioinformatics, etc.。Presiding or participating as a key personnel in public postgraduate projects including national construction of high-level universities、Hunan Province Graduate Innovation Project、National Natural seabet app download Foundation of China Youth Project、Multiple scientific research projects including national key research and development plans。
