Debugging hierarchical relations in large biomedical ontologies: finding needles in a haystack
报告题目：Debugging hierarchical relations in large biomedical ontologies: finding needles in a haystack
崔丽聪(https://sbmi.uth.edu/cuilab/index.htm)，学士和硕士毕业于陕西师范大学，博士毕业于美国凯斯西储大学(Case Western Reserve University)，曾任美国肯塔基大学(University of Kentucky)计算机系助理教授，现任德克萨斯大学休斯顿健康科学中心(University of Texas Health Science Center at Houston)助理教授, 博士生导师，研究方向为生物医学信息。在Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, Bioinformatics, IEEE Journal of Biomedical and Health Informatics等国际期刊和会议发表论文70余篇，Google Scholar引用超过1200次。主持美国国家自然科学基金项目3项以及国立卫生研究院项目4项，并参与国立卫生研究院项目3项。
Ontologies have been used in a wide variety of biomedical applications including information extraction and retrieval, data integration and management, clinical decision support. Quality defects of biomedical ontologies, if not addressed, could affect all downstream applications that use them as knowledge sources. However, identification of potential quality defects is challenging due to the ever-growing size and complexity of biomedical ontologies (i.e., large and evolving graphs). We develop principled and machine learning-based approaches to effectively detect missing or erroneous hierarchical relations in large ontologies including SNOMED CT and Gene Ontology.