An ontology-based approach to linking model organisms and resources to human diseases

Christopher J. Mungall, David Anderson, Anita Bandrowski, Brian Canada, Andrew Chatyr-Aryamontri, Keith Cheng, P. Michael Conn, Kara Dolinski, Mark Ellisman, Janan Eppig, Jeffrey S. Grethe, Joseph Kemnitz, Shawn Iadonato, Stephen D. Larson, Charles Magness, Marvann E. Martone, Mike Tyers, Carlo Torniai, Olga Troyanskaya, Judith TurnerMonte Westerfield, Melissa A. Haendel

Research output: Contribution to journalConference articlepeer-review


The scientific community has invested heavily in the creation of genetically modified organisms, other model systems, and large genetic screens because they greatly inform our understanding of human disease. However, it remains difficult to identify organisms suitable for one's research because information about them is not readily accessible. The initiative to Link Animal Models to Human Disease (LAMHDI; was developed to allow users to search for a diverse set of models of disease using both curated disease-model links and inferred paths based on gene orthology and pathway membership. These inferences are made by traversing connections between records in publicly available data from resources such as the Online Mendelian Inheritance in Man (OMIM). Medical Subject Headings (MeSH), EntrezGene. Homologene. and WikiPathways. This allows researchers to rapidly explore and identify a wide range of model systems, visualizing the multi-step genetic relationship between disease and model. However, if LAMHDI were able to semantically link an organism's phenotypic attributes to diseases, genes, expression profiles, etc. their relevance and utility to a given line of research would be much more greatly illuminated and new novel insights between disease, genetics and phenotype discovered.

Original languageEnglish (US)
Pages (from-to)263-265
Number of pages3
JournalCEUR Workshop Proceedings
StatePublished - 2011
Event2nd International Conference on Biomedical Ontology, ICBO 2011 - Buffalo, NY, United States
Duration: Jul 26 2011Jul 30 2011

All Science Journal Classification (ASJC) codes

  • General Computer Science


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