Today, ontologies are critical instruments for biomedical investigators, especially in those areas, such as cancer research, that require the command of a vast amount of information and a systemic approach to the design and interpretation of experiments. In fact, ontologies are proliferating in all areas of biomedical research, offering both challenges and opportunities. One of the principal challenges to the full realization of their potential stems from the fact that ontologies are developed in isolation, rendering it impossible to move, for instance, from genes to organisms, to diseases, to drugs. The National Center for Biomedical Ontology (NCBO) represents a fundamental endeavor in the collection, coordination and distribution of biomedical ontologies and offers an unparalleled opportunity to combine biomedical ontologies into a single search space where genetic, anatomic, molecular and pharmacological information can be seamlessly explored as a holistic representation of biomedical knowledge. Unfortunately, ontology integration using standard means of manual curation is a labor intensive task, unable to scale up and keep up with the current growth rate of biomedical ontologies. We have developed a systematic framework for automated ontology engineering based on information theory, and we have successfully applied it to the analysis and engineering of Gene Ontology, the development gene and protein databases, and the identification of peripheral biomarkers of disease progression and drug response. This project brings together a unique group of competences, ranging from ontology engineering, statistics, artificial intelligence, bioinformatics, cancer research, and clinical pharmacogenomics, to develop a principled method, grounded on the mathematics of information theory, to automatically combine and integrate biomedical ontologies and implement it as part of the NCBO web services. Our framework and implementation will be evaluated by comparing its results to those obtained by human curation. The translational impact of this approach will be shown by combining disease, tissue, molecular and drug ontologies to reposition compounds for the treatment of colorectal cancer. This project will integrate biomedical knowledge along dimensions that are today isolated. In so doing, it will empower investigators with a new holistic understanding of disease, it will fast track the clinical translation of biological discoveries by revealing their implications , and it will change our approach to biomedical discovery, especially for those complex diseases that, like cancer, require a systemic view of their biological mechanisms. Ontologies are critical instruments for biomedical investigators especially in those areas, such as cancer research, that require a vast amount of information and a systemic approach to the design and interpretation of their experiments. In collaboration with the National Center for Biomedical Ontology (NCBO), this project will develop a principled method, grounded on the mathematics of information theory, to automatically combine biomedical ontologies. As a result, this project will integrate biomedical knowledge along dimensions that are today isolated and, in so doing, it will empower investigators with a new holistic understanding of disease, it will fast track the clinical translation of biological discoveries, and it will change the approach to discovery, especially for those diseases that, like cancer, require a systemic view of their biological mechanisms

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG004836-02
Application #
7945368
Study Section
Special Emphasis Panel (ZRG1-BST-E (50))
Program Officer
Bonazzi, Vivien
Project Start
2009-09-30
Project End
2013-08-31
Budget Start
2010-09-30
Budget End
2013-08-31
Support Year
2
Fiscal Year
2010
Total Cost
$428,079
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Zollanvari, Amin; Alterovitz, Gil (2017) SNP by SNP by environment interaction network of alcoholism. BMC Syst Biol 11:19
Warner, Jeremy L; Denny, Joshua C; Kreda, David A et al. (2015) Seeing the forest through the trees: uncovering phenomic complexity through interactive network visualization. J Am Med Inform Assoc 22:324-9
Warner, Jeremy L; Zollanvari, Amin; Ding, Quan et al. (2013) Temporal phenome analysis of a large electronic health record cohort enables identification of hospital-acquired complications. J Am Med Inform Assoc 20:e281-7
Warner, Jeremy; Yang, Peter; Alterovitz, Gil (2013) Automated synthesis and visualization of a chemotherapy treatment regimen network. Stud Health Technol Inform 192:62-6
Warner, Jeremy L; Alterovitz, Gil; Bodio, Kelly et al. (2013) External phenome analysis enables a rational federated query strategy to detect changing rates of treatment-related complications associated with multiple myeloma. J Am Med Inform Assoc 20:696-9
Deng, Michelle; Zollanvari, Amin; Alterovitz, Gil (2012) A bayesian translational framework for knowledge propagation, discovery, and integration under specific contexts. AMIA Jt Summits Transl Sci Proc 2012:25-34
Quo, Chang F; Kaddi, Chanchala; Phan, John H et al. (2012) Reverse engineering biomolecular systems using -omic data: challenges, progress and opportunities. Brief Bioinform 13:430-45
Parikh, Neena; Zollanvari, Amin; Alterovitz, Gil (2012) An automated bayesian framework for integrative gene expression analysis and predictive medicine. AMIA Jt Summits Transl Sci Proc 2012:95-104
Marwah, Kshitij; Katzin, Dustin; Zollanvari, Amin et al. (2012) Context-specific ontology integration: a bayesian approach. AMIA Jt Summits Transl Sci Proc 2012:79-86
Zollanvari, Amin; Saccone, Nancy L; Bierut, Laura J et al. (2011) Is the reduction of dimensionality to a small number of features always necessary in constructing predictive models for analysis of complex diseases or behaviours? Conf Proc IEEE Eng Med Biol Soc 2011:3573-6

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