Our understanding of human disease is vitally informed by the use of model systems to investigate disease mechanisms and therapies. Because identifying disease models is so critical, a significant effort has been made to connect researchers to potential models of disease by bringing together information about diseases and model organisms via gene orthology and pathway links in LAMHDI, the initiative to Link Animal Models to Human Disease portal (www.lamhdi.org). However, LAMHDI is currently incomplete in that in vitro biological models are not yet included and one cannot identify model systems based on phenotype similarity to a human disease. Furthermore, there is no mechanism by which to prioritize the myriad of relationships between disease and their models, making both identification of candidate models and discovery of new relationships difficult. The goal of this work is to facilitate the identification of models for disease research, make better use of existin model organisms and in vitro resources and data about them, and provide the ability to uncover new relationships between disease, phenotypes and genes that will further our understanding of disease. To this end we propose to: 1) Enable computation of candidate disease models based on semantic similarity of phenotypes using imported and aligned phenotype data from humans and model organisms. We will include expression data to refine search of phenotypes based on presence of expression within a particular anatomical location and/or genotype. 2) Expand semantic linkage between diseases and in vitro model systems within LAMHDI, including resources such as biospecimens and cell lines from the eagle-i project and external sources. This will permit investigators to identify candidate in vitro model systems based on phenotype or genetic basis. 3) Create a discovery tool to refine searches and to uncover novel relationships between diseases, model organisms, and in vitro resources using genetic, pathway, and phenotype relationships. To bring together the disparate data required, we leverage semantic web technologies and sophisticated information modeling combined with computational algorithms. Usability studies will inform the iterative development of the new knowledge-guided discovery LAMDHI interface.

Public Health Relevance

(provided by applicant): This work proposed here will facilitate the identification of model systems for disease research. This will be accomplished by bringing together data from many different organisms, in vitro resources, and diseases in one central web portal. We believe that this system will not only make better use of our existing organisms and resources and data about them, but it will provide the ability to uncover new information about disease that will further our understanding of disease mechanisms and potential therapies.

National Institute of Health (NIH)
Office of The Director, National Institutes of Health (OD)
Resource-Related Research Projects (R24)
Project #
Application #
Study Section
National Center for Research Resources Initial Review Group (RIRG)
Program Officer
Watson, Harold L
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Oregon Health and Science University
Schools of Medicine
United States
Zip Code
Dolman, Lena; Page, Angela; Babb, Lawrence et al. (2018) ClinGen advancing genomic data-sharing standards as a GA4GH driver project. Hum Mutat 39:1686-1689
Vasilevsky, Nicole A; Foster, Erin D; Engelstad, Mark E et al. (2018) Plain-language medical vocabulary for precision diagnosis. Nat Genet 50:474-476
Wimalaratne, Sarala M; Juty, Nick; Kunze, John et al. (2018) Uniform resolution of compact identifiers for biomedical data. Sci Data 5:180029
Arachchi, Harindra; Wojcik, Monica H; Weisburd, Benjamin et al. (2018) matchbox: An open-source tool for patient matching via the Matchmaker Exchange. Hum Mutat 39:1827-1834
Rozman, Jan; Rathkolb, Birgit; Oestereicher, Manuela A et al. (2018) Identification of genetic elements in metabolism by high-throughput mouse phenotyping. Nat Commun 9:288
Köhler, Sebastian; Vasilevsky, Nicole A; Engelstad, Mark et al. (2017) The Human Phenotype Ontology in 2017. Nucleic Acids Res 45:D865-D876
Schubach, Max; Re, Matteo; Robinson, Peter N et al. (2017) Imbalance-Aware Machine Learning for Predicting Rare and Common Disease-Associated Non-Coding Variants. Sci Rep 7:2959
Sobreira, Nara L M; Arachchi, Harindra; Buske, Orion J et al. (2017) Matchmaker Exchange. Curr Protoc Hum Genet 95:9.31.1-9.31.15
Forslund, Kristoffer; Pereira, Cecile; Capella-Gutierrez, Salvador et al. (2017) Gearing up to handle the mosaic nature of life in the quest for orthologs. Bioinformatics :
Manolio, Teri A; Fowler, Douglas M; Starita, Lea M et al. (2017) Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research. Cell 169:6-12

Showing the most recent 10 out of 52 publications