Knowledge Graphs and Graph Data Science
Dr. Chuming Chen
Research Associate Professor
Department of Computer and Information Sciences
Data Science Institute
Center for Bioinformatics and Computational Biology
University of Delaware
Abstract: Knowledge graphs, a specialized type of graph data representing entities and their relationships, have emerged as a powerful tool in various domains for representing and reasoning about complex relationships in data, including bioinformatics. Graph Data Science (GDS) is a branch of data science that uses graph algorithms and machine learning to analyze data represented as graphs by extracting valuable insights from these interconnected knowledge structures. This seminar will introduce the fundamentals of knowledge graphs and their applications in bioinformatics. We will delve into key concepts such as graph representation, graph construction, and graph query languages. Additionally, we will discuss a range of graph data science and machine learning algorithms (e.g. node embedding and link prediction), and their uses in bioinformatics such as predicting kinase substrate interactions. By the end of this seminar, attendees will have a solid understanding of the potential of the knowledge graphs, graph data science, and their applications in bioinformatics and data science research.
Bio: Dr. Chuming Chen is a Research Associate Professor of the Department of Computer and Information Sciences, Data Science Institute, Center for Bioinformatics and Computational Biology, and Protein Information Resource at the University of Delaware. He earned his Ph.D. in Computer Science and Engineering from the University of South Carolina in 2008. He has more than 20 years of experience working in the fields of computational biology, bioinformatics, semantic web, ontology and knowledge graph. He has contributed to several UniProt Consortium and Protein Ontology Consortium projects. He has also made significant contributions to the development of bioinformatics tools and resources. His current research includes data science, machine learning, and health informatics. Dr. Chen serves on the editorial board for Nature journal Scientific Data. He is a professional member of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE).
