The NCBO creates tools and services that provide the ontological backbone for a broad range of scientific disciplines. We are producing a scalable, distributed infrastructure for publishing, managing, and evaluating ontologies. We are also creating ontology-based annotations of biomedical data sources to advance biomedical, clinical, and translational research. In the next five years, we will continue to grow the BioPortal library and services that will enable scientists to custom-tailor library components and to embed these components directly in their applications. The first generation of BioPortal focused on the content and services for the bio-ontology community. The next generation will provide additional content and services for the broader community of biomedical, clinical, and translational researchers, with an emphasis on generalizability and wide-scale adoption. In many cases, the ontology needs of a research area evolve quickly and researchers must be able to add new ontology terms and to refine and extend existing ones in order to meet community and institutional requirements. Our goal is to enable "agile ontology development", the incremental, user-guided, needs-driven evolution of ontologies. To accomplish this goal, we will integrate the infrastructure for ontology publishing, review, and application with the ontology-revision process, to create a common infrastructure for user feedback, change proposals, and updates by developers. We will use the annotation tools that we have already developed to annotate automatically a large set of public biomedical resources, creating a comprehensive index of ontology-based annotations for enabling translational discoveries. Finally, we will develop analytic methods to profile biomedical data sets for enrichment against the background of these annotations. As a result, just as biologists use the Gene Ontology to determine biological processes over-represented (or enriched) in a set of differentially expressed genes, clinical and translational investigators will be able to determine enrichment of terms representing diseases (or class of diseases), drugs, or other controlled terms in data sets of their choice.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54HG004028-10
Application #
8737920
Study Section
Special Emphasis Panel (ZRG1-BST-K)
Project Start
Project End
Budget Start
2014-08-01
Budget End
2015-07-31
Support Year
10
Fiscal Year
2014
Total Cost
$1,426,826
Indirect Cost
$455,620
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Zykovich, Artem; Hubbard, Alan; Flynn, James M et al. (2014) Genome-wide DNA methylation changes with age in disease-free human skeletal muscle. Aging Cell 13:360-6
Ghebremariam, Yohannes T; Lee, Jerry C; LePendu, Paea et al. (2014) Response to letters regarding article, "unexpected effect of proton pump inhibitors: elevation of the cardiovascular risk factor asymmetric dimethylarginine". Circulation 129:e428
Wu, Stephen T; Juhn, Young J; Sohn, Sunghwan et al. (2014) Patient-level temporal aggregation for text-based asthma status ascertainment. J Am Med Inform Assoc 21:876-84
Walls, Ramona L; Deck, John; Guralnick, Robert et al. (2014) Semantics in support of biodiversity knowledge discovery: an introduction to the biological collections ontology and related ontologies. PLoS One 9:e89606
Lopez-Garcia, Pablo; Lependu, Paea; Musen, Mark et al. (2014) Cross-domain targeted ontology subsets for annotation: the case of SNOMED CORE and RxNorm. J Biomed Inform 47:105-11
Mort, Matthew; Sterne-Weiler, Timothy; Li, Biao et al. (2014) MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing. Genome Biol 15:R19
Wass, Mark N; Mooney, Sean D; Linial, Michal et al. (2014) The automated function prediction SIG looks back at 2013 and prepares for 2014. Bioinformatics 30:2091-2
Harpaz, Rave; Callahan, Alison; Tamang, Suzanne et al. (2014) Text mining for adverse drug events: the promise, challenges, and state of the art. Drug Saf 37:777-90
Jung, Kenneth; LePendu, Paea; Chen, William S et al. (2014) Automated detection of off-label drug use. PLoS One 9:e89324
Huang, Sandy H; LePendu, Paea; Iyer, Srinivasan V et al. (2014) Toward personalizing treatment for depression: predicting diagnosis and severity. J Am Med Inform Assoc 21:1069-75

Showing the most recent 10 out of 85 publications