Contribution of model organism phenotypes to the computational identification of human disease genes.

ABSTRACT: Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype-phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of kno

SUBMITTER: Alghamdi SM 

PROVIDER: S-EPMC9366895 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature