Approaches to discover new human disease models through Boolean relationships of orthologous phenotypes
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In the development of genome-wide databases of model organisms, non-traditional approaches to study genomic networks have emerged to model molecular interactions. Past approaches to model such systems have used the homology of genes in model organisms to study human diseases and conditions. Combining the homology of genes between human model organisms with information in genomic databases, the Marcotte laboratory has discovered a systematic approach to predict new candidate genes for human diseases. In characterizing phenotypes of model organisms with homologous genes, it is possible to reveal similar genetic interactions of homologous genes in human diseases. These phenotypes are characterized by the presence and absence of all orthologous genes between species, these binary data structures are called phenologs. The project below examined the potential of Boolean relationships of phenologs within one or multiple species to optimize the identified set of genes for a human disease and to predict more candidate genes involved in human disease.