Computational Approaches for Human Disease Gene Prediction and Ranking

  • Cheng Zhu
  • Chao Wu
  • Bruce J. Aronow
  • Anil G. Jegga
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 799)

Abstract

While candidate gene association studies continue to be the most practical and frequently employed approach in disease gene investigation for complex disorders, selecting suitable genes to test is a challenge. There are several computational approaches available for selecting and prioritizing disease candidate genes. A majority of these tools are based on guilt-by-association principle where novel disease candidate genes are identified and prioritized based on either functional or topological similarity to known disease genes. In this chapter we review the prioritization criteria and the algorithms along with some use cases that demonstrate how these tools can be used for identifying and ranking human disease candidate genes.

Keywords

Lecithin Retinol 

Supplementary material

216120_1_En_4_MOESM1_ESM.xlsx (497 kb)
20-Fine needle aspiration of solid component of complex nodu (XLSX 497 KB)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Cheng Zhu
    • 1
    • 2
  • Chao Wu
    • 1
    • 2
  • Bruce J. Aronow
    • 3
    • 2
  • Anil G. Jegga
    • 3
    • 2
  1. 1.Department of Computer Science, College of Engineering and Applied ScienceUniversity of CincinnatiCincinnatiUSA
  2. 2.Division of Biomedical InformaticsCincinnati Children’s Hospital Medical CenterCincinnatiUSA
  3. 3.Department of PediatricsUniversity of Cincinnati College of MedicineCincinnatiUSA

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