Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 350578
Title A text-mining analysis of the human phenome
Author(s) Driel, M.A. van; Bruggeman, J.; Vriend, G.; Brunner, H.G.; Leunissen, J.A.M.
Source European Journal of Human Genetics 14 (2006)5. - ISSN 1018-4813 - p. 535 - 542.
DOI https://doi.org/10.1038/sj.ejhg.5201585
Department(s) Bioinformatics
Publication type Refereed Article in a scientific journal
Publication year 2006
Keyword(s) saccharomyces-cerevisiae - protein families - gene ontology - genome - identification - biology - knowledgebase - products - database - disease
Abstract A number of large-scale efforts are underway to define the relationships between genes and proteins in various species. But, few attempts have been made to systematically classify all such relationships at the phenotype level. Also, it is unknown whether such a phenotype map would carry biologically meaningful information. We have used text mining to classify over 5000 human phenotypes contained in the Online Mendelian Inheritance in Man database. We find that similarity between phenotypes reflects biological modules of interacting functionally related genes. These similarities are positively correlated with a number of measures of gene function, including relatedness at the level of protein sequence, protein motifs, functional annotation, and direct protein¿protein interaction. Phenotype grouping reflects the modular nature of human disease genetics. Thus, phenotype mapping may be used to predict candidate genes for diseases as well as functional relations between genes and proteins. Such predictions will further improve if a unified system of phenotype descriptors is developed. The phenotype similarity data are accessible through a web interface at http://www.cmbi.ru.nl/MimMiner/
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