Semantic LAMHDI: Linking diseases to model organism resources

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DESCRIPTION (provided by applicant): 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.