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={Nicki Tiffin and Euan A. Adie and Frances S. Turner and Han G. Brunner and Marc A. van Driel and Martin Oti and N{\'u}ria L{\'o}pez-Bigas and Christos A. Ouzounis and Carolina Perez-Iratxeta and Miguel Andrade and Adebowale A. Adeyemo and Mary Elizabeth Patti and Colin A. 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… 

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