6月15日:Guo-Qiang Zhang
发布时间:2017-06-06  阅读次数:1162

 

报告题目:Big Order Computing, with Applications in Ontological Engineering

报告人:Guo-Qiang Zhang 教授 (University of Kentucky)

主持人:吴恒洋

报告时间:2017年6月15日10:00—11:00

报告地点:数学馆201报告厅

 

报告摘要:

Ontologies are shared conceptualizations of a domain represented in a formal language. They represent not only the concepts used in scientific work, but just as importantly, the relationships between the concepts.Concepts in an ontology are organized into a concept hierarchy with more generic concepts modeled as parents of more specific concepts. Such hierarchical relationships allow us to think of an ontology as a poset, where concepts are represented as nodes and relationships are represented as edges. In biomedicine, the Unified Medical Language System (UMLS), managed by the US National Library of Medicine, is perhaps the largest integrated repository of biomedical ontologies. The 2014AA release of UMLS covers over 2.9 million concepts from more than 140 source ontologies.  Visualization and continued quality assurance of such ontologies is an integral part of their lifecycle. The computational tasks involved in processing such large posets are challenging and interesting. This talk will focus on the following aspects of big order computing: 

* Elastic parallel processing of posets, representing ontologies, using MapReduce, a cloud computing framework; 

* Scalable extraction and visualization of substructures from posets, such as lattice and non-lattice fragments; 

* A practically linear-time algorithm for finding all lowest common ancestors for all non-trivial pairs, achieving orders of magnitude in performance enhancement for extracting poset fragments; 

* Comparative evaluation of an exemplar medical ontology, SNOMED CT, using a novel method called Retrospective Ground-Truthing, as a surrogate reference standard for evaluating the performance of automated Ontology Quality Assurance (OQA) methods.

 

报告人简介:

Dr. Zhang is Professor of Internal Medicine and Computer Science at the University of Kentucky, where he is the Director of the Institute of Biomedical Informatics;  Chief of the Division of Biomedical Informatics

(https://bmi.med.uky.edu); Associate Director, Center for Clinical & Translational Science (http://www.ccts.uky.edu/ccts/BMI_Core);and Director, Informatics & Data Analytics Core, NINDS-CWW Center for SUDEP Research(sudepresearch.org). Prior to joining the University of Kentucky, his role at Case Western Reserve University included Division Chief of Medical Informatics, Co-Director of Biomedical Research Information Management Core of the Case Western CTSA, and Associate Director for Case Comprehensive Cancer Center while performing duties as a tenured professor in the Case School of Engineering.

 

His research interests include data science and bigdata in biomedicine, large-scale, multi-center data integration, biomedical ontology development, information retrieval, query interface design, and agile, interface-driven, access-control grounded software development. These interests are reflected in his over 130 publications inComputer Science and Biomedical Informatics. Over more than a decade, Dr. Zhang has developed a range of clinical research informatics tools for data capturing, data management, cohort discovery, and clinical decision support, such as VISAGE (PMID: 21347154), MEDCIS (PMID: 23686934), OnWARD (PMID: 21924379), OPIC (PMID: 23304354), EpiDEA (PMID: 23304396), and Cloudwave (PMID: 23920671). Supported by multiple federal- and foundation-funded awards and acclimated in a multi-disciplinary team-science, collaborative setting, Dr. Zhang effectively brings cutting-edge computer science and informatics methodology to addressing biomedical data/big data challenges through the translation of theory, algorithms, methods and best practices to functional and usable tools impacting the entire clinical research data lifecycle.

华东师范大学计算机科学与软件工程学院
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