A text-mining analysis of the human phenome

M.A. van Driel, J. Bruggeman, G. Vriend, H.G. Brunner, J.A.M. Leunissen

Research output: Contribution to journalArticleAcademicpeer-review

434 Citations (Scopus)

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/
Original languageEnglish
Pages (from-to)535-542
JournalEuropean Journal of Human Genetics
Volume14
Issue number5
DOIs
Publication statusPublished - 2006

Keywords

  • saccharomyces-cerevisiae
  • protein families
  • gene ontology
  • genome
  • identification
  • biology
  • knowledgebase
  • products
  • database
  • disease

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