Department of Biological Statistics and Computational Biology and Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14850, USA.
Brief Funct Genomics. 2011 Sep;10(5):280-93. doi: 10.1093/bfgp/elr024. Epub 2011 Jul 15.
Despite the considerable progress in disease gene discovery, we are far from uncovering the underlying cellular mechanisms of diseases since complex traits, even many Mendelian diseases, cannot be explained by simple genotype-phenotype relationships. More recently, an increasingly accepted view is that human diseases result from perturbations of cellular systems, especially molecular networks. Genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks. Such observations have built the basis for a large collection of computational approaches to find previously unknown genes associated with certain diseases. The majority of the methods are based on protein interactome networks, with integration of other large-scale genomic data or disease phenotype information, to infer how likely it is that a gene is associated with a disease. Here, we review recent, state of the art, network-based methods used for prioritizing disease genes as well as unraveling the molecular basis of human diseases.
尽管在疾病基因发现方面取得了相当大的进展，但我们远未揭示疾病的潜在细胞机制，因为复杂的特征，即使是许多孟德尔疾病，也不能用简单的基因型-表型关系来解释。最近，越来越多的人认为，人类疾病是由细胞系统的干扰引起的，特别是分子网络。与同一种或类似疾病相关的基因通常位于分子网络的同一区域。这些观察结果为发现以前未知的与某些疾病相关的基因提供了大量计算方法的基础。大多数方法都是基于蛋白质相互作用网络，并整合其他大规模基因组数据或疾病表型信息，以推断一个基因与疾病相关的可能性。在这里，我们回顾了最近基于网络的用于优先考虑疾病基因以及揭示人类疾病分子基础的方法。
