Ph.D. EXIT SEMINAR – Warren Cheung

Ph.D. EXIT SEMINAR
Warren Cheung
B.Sc., Computer Science and Microbiology at UBC, 2003
M.Sc, Computer Science at UBC, 2007
Friday, July 6th, 2012 at 11 AM
LOCATION: Lecture Theatre, BCCRC
Inferring Novel Relationships through Over-Representation Analysis of Medical Subjects in Biomedical Bibliographies
Abstract:

MEDLINE®/PubMed® is a richly annotated resource of over 21 million article citations, growing at a modern rate of over 600,000 citations annually. One grand challenge of bioinformatics is analysing the extensive literature for a biomedical entity such as a gene or disease. We explore using over-representation to extract pertinent biomedical annotation from the research articles for an entity. The quantitative profiles generated are compared to predict novel associations between entities.
Medical Subject Heading Over-Representation Profiles (MeSHOPs) are constructed from the primary literature of an entity of interest. Medical subject annotations for each article are extracted. Statistical tests evaluate the significance of each term’s frequency across the set of articles, compared against an appropriate background set. The resulting MeSHOP is composed of each term and corresponding enrichment p-value.
MeSHOPs can be computed for any entity with an associated bibliography of PubMed articles. We evaluate the predictive performance of quantitatively comparing MeSHOPs to discover novel associations between gene and disease entities, achieving up to 16% improvement in accuracy compared to gene or disease baseline features (measured as increased Receiver Operating Characteristic Area Under the Curve). Strong literature annotation level bias on the predictive performance for future gene-disease association was seen. We observe similar results in a parallel analysis of associations between drugs and disease.
Efficiently identifying authors with similar research interests is a challenge in science. During the peer review process, authors seek scientists with similar expertise. MeSHOPs are generated for individual authors, identifying their research foci. Extending the methods to allow comparison across large sets of entities, overlapping research interests between researchers were identified. The predictive performance was evaluated for capacity to identify authors working in the same research domains.
Biomedical annotation analysis of primary literature provides insight into the areas of research focus, and is demonstrated to link entities through similarities in their MeSHOPs. We quantitatively confirm the trend where well-studied genes, diseases and drugs are more likely to be the focus of further research. MeSHOP analysis demonstrates that knowledge in the annotated primary literature can be efficiently mined, and the untapped knowledge therein can be discovered computationally.

Supervisors: Francis Ouellette, Ontario Institute for Cancer Research, Department of Cell and Systems Biology, University of Toronto, Toronto, ON, Canada
Wyeth W. Wasserman, Centre for Molecular Medicine and Therapeutics at the Child and Family Research Institute, Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada