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   THE DISSERTATION DEFENSE FOR THE DEGREE OF Ph.D. IN COMPUTER SCIENCE FOR

                       			Woei-Jyh Lee

   Will be held:

   DATE:            	Friday December 12, 2008 at 2:00 p.m.
   LOCATION:       	Room 3118 Bldg. 296

   TITLE:            	A Framework for Discovering Meaningful Associations
in the Annotated Life Sciences Web

ABSTRACT:  During the last decade, life sciences researchers have gained
access to the entire human genome, reliable high-throughput biotechnologies,
affordable computational resources and public network access. In
combination, these new tools created a new model for life sciences research
that no longer uses computational tools merely to monitor research, but
instead exploits these tools to acquire knowledge and make discoveries.
Consider a simplified Web of three publicly accessible data resources Entrez
Gene, PubMed and OMIM. Data records in each resource are annotated with
terms from multiple controlled vocabularies (CVs). The links between data
records in two resources form a relationship between the two resources.
Thus, a record in Entrez Gene, annotated with GO terms, can have links to
multiple records in PubMed that are annotated with MeSH terms. Similarly,
OMIM records, annotated with terms from SNOMED, may have links to records in
Entrez Gene and PubMed. This forms a rich Web of annotated data records.

We name this work Life Science Link (LSLink). The objective in this research
is to develop methodology and tools to discover meaningful patterns across
resources and CVs. As a first stage in engineering patterns, we execute a
protocol to follow links, extract annotations, and generate datasets of
termlinks, which consist of data records and CV terms. We then mine the
termlinks of the datasets to find potentially meaningful associations.
Biologically meaningful associations of pairs of CV terms may yield
innovative nuggets of previously unknown knowledge.  Moreover, the bridge of
associations across CV terms will reflect the practice of how scientists
annotate data across linked data repositories.

Contributions include deploying the methodology for datasets, developing
metrics for mining, applying semantic knowledge for generalization,
implementing tools for discovery, and validating with multiple biological
use cases.  Inspired by research in association rule mining and linkage
analysis, we develop an approach based on the logarithm of the odds (LOD)
measure to determine support and confidence scores in the associations.
Biologists may then analyze those associations that score high in the scores
to explore significant termlinks that may lead to new knowledge. In order to
validate the LOD-based support and confidence metrics, we develop a
secondary test for significance based on the hypergeometric distribution
from the hypothesis test.  We compare the observed score with the
expectation score to check if the association is over-represented.  We later
aggregate termlinks over siblings of a common parent CV term and use them as
additional evidence to boost the support and confidence scores in the
associations of the parent CV term. The result of associations and scores
can be reviewed online using one of the discovery tools, which also supports
statistics and filtering options. Finally, the cancer informatics use case
validates the associations between human genes and diseases, while the plant
biology use case validates the associations between Arabidopsis genes and
phenotypes.

In future work, we can extend the dataset to include additional links and
annotations.  We can extend the methodology to include more semantic
knowledge associated with the CV terms.  Last but not least, we can
establish more biological use cases to validate and prove the success of our
framework for discovering meaningful association in the annotated life
sciences Web.

Examining Committee:

COMMITTEE CHAIR:       	 Dr. Louiqa Raschid
Dean's Representative: 		 Dr. Stephen M. Mount
Committee Member(s):   	 Dr. Mihai Pop
                        		 Dr. Carl Kingsford
				 Dr. Jimmy Lin

   EVERYONE IS INVITED TO ATTEND THE PRESENTATION PORTION OF THIS DEFENSE