The rapid and continuing proliferation of on-line biomedical information sources has necessitated the creation of software tools for automatically navigating these networks, identifying relevant documents, and extracting the desired information from them. Designing, implementing, and evaluating such a software system is the primary objective of this project. More specifically, a central aim is to create a system that can be personalized to the particular and changing interests of individual biomedical scientists. Machine leaming methods play a central role in our software assistant allowing the system to adapt to individual users. A flexible language with which scientists can communicate their interests to the machine leaming algorithms will be developed. Machine leaming methods typically have the weakness of requiring user-provided training examples, but creating such examples is usually a burdensome task. Hence, another central aim is to greatly reduce the need for human-labeled training data by creating techniques that obtain these examples by indirect methods. The system being developed will be field-tested in the laboratories of several biomedical researchers.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM007050-03
Application #
6528413
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
2000-09-29
Project End
2005-09-28
Budget Start
2002-09-29
Budget End
2005-09-28
Support Year
3
Fiscal Year
2002
Total Cost
$288,000
Indirect Cost
Name
University of Wisconsin Madison
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
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Vlachos, Andreas; Craven, Mark (2012) Biomedical event extraction from abstracts and full papers using search-based structured prediction. BMC Bioinformatics 13 Suppl 11:S5
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Andrzejewski, David; Zhu, Xiaojin; Craven, Mark (2009) Incorporating Domain Knowledge into Topic Modeling via Dirichlet Forest Priors. Proc Int Conf Mach Learn 382:25-32
Smith, Adam A; Craven, Mark (2008) Fast multisegment alignments for temporal expression profiles. Comput Syst Bioinformatics Conf 7:315-26
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Noto, Keith; Craven, Mark (2006) A specialized learner for inferring structured cis-regulatory modules. BMC Bioinformatics 7:528
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