Tiffin Nicki
The South African National Bioinformatics Institute, University of the Western Cape, 7925, Belville, Cape Town, South Africa.
Methods Mol Biol. 2011;760:175-87. doi: 10.1007/978-1-61779-176-5_11.
Prioritization of most likely etiological genes entails predicting and defining a set of characteristics that are most likely to fit the underlying disease gene and scoring candidates according to their fit to this "perfect disease gene" profile. This requires a full understanding of the disease phenotype, characteristics, and any available data on the underlying genetics of the disease. Public databases provide enormous and ever-growing amounts of information that can be relevant to the prioritization of etiological genes. Computational approaches allow this information to be retrieved in an automated and exhaustive way and can therefore facilitate the comprehensive mining of this information, including its combination with sets of empirically generated data, in the process of identifying most likely candidate disease genes.
确定最有可能的致病基因需要预测和定义一组最有可能符合潜在疾病基因的特征，并根据候选基因与这种“完美疾病基因”概况的匹配程度对其进行评分。这需要全面了解疾病表型、特征以及有关该疾病潜在遗传学的任何可用数据。公共数据库提供了大量且不断增长的信息，这些信息可能与致病基因的优先级确定相关。计算方法允许以自动化且详尽的方式检索这些信息，因此可以在识别最有可能的候选疾病基因的过程中促进对这些信息的全面挖掘，包括将其与经验生成的数据集相结合。
