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| General Section People Events Program Details |
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Predoctoral Trainee
As DNA sequencing projects complete more genomes, the emphasis
is shifting from sequencing the genetic code to actually understanding
the functions of and relationships between genes and related molecules.
This has lead to a recent boom in biomedical research, improving both
the quality and quantity of biomolecular knowledge with each new publication
(much of this literature being in electronic format). While this is certainly
beneficial to researchers, it also creates a load of information too overwhelming
for a research group - let alone an individual - to read entirely. To
help promote successful research directions, biomedical scientists could
benefit from tools for automated Natural Language Processing (NLP) –
a branch of Artificial Intelligence research seeking ways to perform effective
computerized tasks using natural language. Specifically, we would like
to have intelligent machines that can automatically cull the wealth of
electronic literature, annotate documents, and build structured knowledge-bases
about interactions between proteins, genes, RNA, cell loci, etc. This
information can populate more structured reference databases, be analyzed
with data mining techniques to discover new relationships, and even launch
new research directions. The first step in such a system, however, is
identifying the terms of interest (e.g., genes, mRNA molecules, and proteins)
in these texts. This is the focus of our research. Resumehttp://www.cs.wisc.edu/~bsettles/vita.html PublicationsSearch for publications by Burr Settles (Pub Med, Cite Seer) |
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