Recent developments in text mining research, and in scientific publication, have brought us to the moment when the long-standing potential of natural language processing technology to benefit biomedical researchers may finally be realized. Technological advances, recent results in computational linguistics, maturation of biomedical ontology, and the advent of resources such as PubMedCentral have set the stage for an attempt at an integrated computational analysis of a large proportion of the full text biomedical literature. Such an analysis has the potential to dramatically extend the way that biomedical researchers can effectively use the scientific literature, particularly in the analysis of genome-scale datasets, broadly accelerating and increasing the efficiency of scientific discovery. We hypothesize that it is now possible to extract a wide variety of ontologically-grounded entities and relationships by processing the entire PubMedCentral document collection accurately and with good coverage, to use this extracted information to produce new genres of scientifically valuable tools and analysis techniques, and to demonstrate its utility in the analysis of genome-scale data. The challenges that we plan to overcome range from fundamental linguistic issues (e.g. cross- document coreference resolution) to high-performance computing (e.g. scaling up integrated processing to include millions of complex documents), to fielding practical systems that can exploit enormous knowledge-bases to accelerate the analysis of very large molecular data sets.

Public Health Relevance

Enormous amounts of biomedical information are now available in the PubMedCentral database, but computers cannot work with it because it is in the form of human-language text and humans can't read it all due to its large volume. The goal of this project is to harvest large amounts of that information automatically, making it available to humans in summarized form and to computers in computer-readable form.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
5R01LM009254-08
Application #
8528719
Study Section
Special Emphasis Panel (ZLM1-ZH-C (01))
Program Officer
Sim, Hua-Chuan
Project Start
2006-09-15
Project End
2014-09-29
Budget Start
2013-09-30
Budget End
2014-09-29
Support Year
8
Fiscal Year
2013
Total Cost
$526,738
Indirect Cost
$182,465
Name
University of Colorado Denver
Department
Pharmacology
Type
Schools of Medicine
DUNS #
041096314
City
Aurora
State
CO
Country
United States
Zip Code
80045
Cohen, K Bretonnel; Lanfranchi, Arrick; Choi, Miji Joo-Young et al. (2017) Coreference annotation and resolution in the Colorado Richly Annotated Full Text (CRAFT) corpus of biomedical journal articles. BMC Bioinformatics 18:372
Kao, David P; Stevens, Laura M; Hinterberg, Michael A et al. (2017) Phenotype-Specific Association of Single-Nucleotide Polymorphisms with Heart Failure and Preserved Ejection Fraction: a Genome-Wide Association Analysis of the Cardiovascular Health Study. J Cardiovasc Transl Res 10:285-294
Hooper, Joan E; Feng, Weiguo; Li, Hong et al. (2017) Systems biology of facial development: contributions of ectoderm and mesenchyme. Dev Biol 426:97-114
Eberlein, Jens; Davenport, Bennett; Nguyen, Tom et al. (2016) Aging promotes acquisition of naive-like CD8+ memory T cell traits and enhanced functionalities. J Clin Invest 126:3942-3960
Névéol, Aurélie; Cohen, K Bretonnel; Grouin, Cyril et al. (2016) Clinical Information Extraction at the CLEF eHealth Evaluation lab 2016. CEUR Workshop Proc 1609:28-42
Livingston, Kevin M; Bada, Michael; Baumgartner Jr, William A et al. (2015) KaBOB: ontology-based semantic integration of biomedical databases. BMC Bioinformatics 16:126
Karimpour-Fard, Anis; Epperson, L Elaine; Hunter, Lawrence E (2015) A survey of computational tools for downstream analysis of proteomic and other omic datasets. Hum Genomics 9:28
Mirel, Barbara; Görg, Carsten (2014) Scientists' sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support. BMC Bioinformatics 15:117
Funk, Christopher S; Hunter, Lawrence E; Cohen, K Bretonnel (2014) Combining heterogenous data for prediction of disease related and pharmacogenes. Pac Symp Biocomput :328-39
Hill, David P; Adams, Nico; Bada, Mike et al. (2013) Dovetailing biology and chemistry: integrating the Gene Ontology with the ChEBI chemical ontology. BMC Genomics 14:513

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