Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

@article{Tiffin2006ComputationalDG,
  title={Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes},
  author={N. Tiffin and E. Adie and F. Turner and H. Brunner and M. V. van Driel and M. Oti and N. L{\'o}pez-Bigas and C. Ouzounis and C. Perez-Iratxeta and M. Andrade-Navarro and A. Adeyemo and M. Patti and C. Semple and Winston A Hide},
  journal={Nucleic Acids Research},
  year={2006},
  volume={34},
  pages={3067 - 3081}
}
Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556… Expand
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