Department of Mathematics

Statistics Seminar

2016 - 2017


DATE: Thursday, March 2, 2017

TIME: 3:30 - 4:30pm

ROOM: 1313 Math Building

Ontology-based Biomedical Data Standardization, Integration, and Statistical Analysis

Oliver He , Associate Professor

Department of Microbiology and Immunology
University of Michigan Medical School, Ann Arbor

ABSTRACT: A biomedical ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific biomedical domain and how they relate to each other. In the cutting edge biomedical research, ontologies have played critical roles, for example, serving as advanced controlled terminologies, knowledge bases, metadata standards, and supporting integrative statistical data analysis. The Ontology of Biological and Clinical Statistics (OBCS) is a community-based open source ontology that represents statistics-related terms and their relations in a rigorous fashion. There also exist many domain specific biomedical ontologies, such as the Vaccine Ontology (VO) for the domain of vaccines and vaccination, and the Ontology of Adverse Events (OAE) for the domain of adverse events following various medical interventions. The usage of these and other ontologies supports standard and reproducible data representation and statistical analysis in different biomedical domains. For example, ontologies and ontology-based statistical methods support: (i) advanced literature mining and analysis of vaccine-mediated gene-gene interaction networks, and (ii) data standardization and analysis of clinically reported vaccine and drug adverse event cases. A theory-oriented OneNet framework is finally proposed to integrate different ontologies and ontology-supported statistical approaches for integrative and systematic life science research.