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Identifying Disease Genes Based on Functional Annotation and Text Mining

Author

Listed:
  • Fang Yuan

    (Shenzhen Institute of Information Technology, China)

  • Mingliang Li

    (Shenzhen Institute of Information Technology, China)

  • Jing Li

    (Huawei Technologies Corporation, China)

Abstract

The identification of disease genes from candidated regions is one of the most important tasks in bioinformatics research. Most approaches based on function annotations cannot be used to identify genes for diseases without any known pathogenic genes or related function annotations. The authors have built a new web tool, DGHunter, to predict genes associated with these diseases which lack detailed function annotations. Its performance was tested with a set of 1506 genes involved in 1147 disease phenotypes derived from the morbid map table in the OMIM database. The results show that, on average, the target gene was in the top 13.60% of the ranked lists of candidates, and the target gene was in the top 5% with a 40.70% chance. DGHunter can identify disease genes effectively for those diseases lacking sufficient function annotations.

Suggested Citation

  • Fang Yuan & Mingliang Li & Jing Li, 2011. "Identifying Disease Genes Based on Functional Annotation and Text Mining," International Journal of Advanced Pervasive and Ubiquitous Computing (IJAPUC), IGI Global, vol. 3(1), pages 45-54, January.
    • Handle: RePEc:igg:japuc0:v:3:y:2011:i:1:p:45-54
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