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Use of Model Organism and Disease Databases to Support Matchmaking for Human Disease Gene Discovery
Author(s) -
Mungall Christopher J.,
Washington Nicole L.,
NguyenXuan Jeremy,
Condit Christopher,
Smedley Damian,
Köhler Sebastian,
Groza Tudor,
Shefchek Kent,
Hochheiser Harry,
Robinson Peter N.,
Lewis Suzanna E.,
Haendel Melissa A.
Publication year - 2015
Publication title -
human mutation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.981
H-Index - 162
eISSN - 1098-1004
pISSN - 1059-7794
DOI - 10.1002/humu.22857
Subject(s) - disease , similarity (geometry) , matching (statistics) , organism , clinical phenotype , application programming interface , interface (matter) , computer science , mechanism (biology) , semantic similarity , biology , data science , computational biology , phenotype , information retrieval , gene , artificial intelligence , genetics , medicine , pathology , philosophy , bubble , epistemology , maximum bubble pressure method , parallel computing , programming language , image (mathematics)
The Matchmaker Exchange application programming interface (API) allows searching a patient's genotypic or phenotypic profiles across clinical sites, for the purposes of cohort discovery and variant disease causal validation. This API can be used not only to search for matching patients, but also to match against public disease and model organism data. This public disease data enable matching known diseases and variant–phenotype associations using phenotype semantic similarity algorithms developed by the Monarch Initiative. The model data can provide additional evidence to aid diagnosis, suggest relevant models for disease mechanism and treatment exploration, and identify collaborators across the translational divide. The Monarch Initiative provides an implementation of this API for searching multiple integrated sources of data that contextualize the knowledge about any given patient or patient family into the greater biomedical knowledge landscape. While this corpus of data can aid diagnosis, it is also the beginning of research to improve understanding of rare human diseases.