Network-based global inference of human disease genes.
Network-based global inference of human disease genes.
复制标题
基于网络的人类疾病基因的全局推断。
DOI：
10.1038/msb.2008.27
复制
发表时间：
2008
影响因子：
9.9
通讯作者：
Li, Shao
中科院分区：
文献类型：
--
作者：
Wu, Xuebing;Jiang, Rui;Zhang, Michael Q.;Li, Shao
破译人类疾病的遗传基础是生物医学研究的一个重要目标。基于表型相似疾病是由功能相关基因引起的假设，我们提出了一个整合人类蛋白质-蛋白质相互作用、疾病表型相似性和已知基因-表型关联的计算框架，以捕捉表型和基因型之间的复杂关系。我们开发了一个名为CIPHER的工具来预测和优先排序疾病基因，我们表明人类蛋白质网络和表型网络之间的全局一致性可靠地预测疾病基因。我们的方法适用于遗传上未表征的表型，在疾病基因的全基因组扫描中有效，并可扩展到探索复杂疾病中的基因协同性。预测了超过1000种人类表型的遗传景观，揭示了表型-基因型关系的全球模块化组织。超过5000种人类表型的候选基因的全基因组优先排序，包括那些特征不明显的疾病位点甚至缺乏已知关联的基因，公开发布，以促进未来疾病基因的发现。
Deciphering the genetic basis of human diseases is an important goal of biomedical research. On the basis of the assumption that phenotypically similar diseases are caused by functionally related genes, we propose a computational framework that integrates human protein–protein interactions, disease phenotype similarities, and known gene–phenotype associations to capture the complex relationships between phenotypes and genotypes. We develop a tool named CIPHER to predict and prioritize disease genes, and we show that the global concordance between the human protein network and the phenotype network reliably predicts disease genes. Our method is applicable to genetically uncharacterized phenotypes, effective in the genome-wide scan of disease genes, and also extendable to explore gene cooperativity in complex diseases. The predicted genetic landscape of over 1000 human phenotypes, which reveals the global modular organization of phenotype–genotype relationships. The genome-wide prioritization of candidate genes for over 5000 human phenotypes, including those with under-characterized disease loci or even those lacking known association, is publicly released to facilitate future discovery of disease genes.
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影响因子：
12.3
作者：
Aloy P
通讯作者：
Aloy P
影响因子：
12.3
作者：
Fraser HB;Plotkin JB
通讯作者：
Plotkin JB
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
DOI：
10.1073/pnas.0407365101
发表时间：
2004-11-16
影响因子：
11.1
作者：
Das, D;Banerjee, N;Zhang, MQ
通讯作者：
Zhang, MQ
影响因子：
5.8
作者：
Gaulton, Kyle J.;Mohlke, Karen L.;Vision, Todd J.
通讯作者：
Vision, Todd J.