A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome.
A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome.
复制标题
DOI：
10.1371/journal.pcbi.1004120
复制
发表时间：
2015-04
影响因子：
4.3
通讯作者：
Barabási AL
中科院分区：
文献类型：
--
作者：
Ghiassian SD;Menche J;Barabási AL
疾病相关蛋白经常相互作用的观察促进了基于网络的方法的发展，以阐明人类疾病的分子机制。这种方法建立在这样的假设上，即蛋白质相互作用网络可以被视为地图，其中疾病可以在一定的邻域内通过局部扰动来识别。因此，这些社区或疾病模块的识别是详细调查特定病理表型的先决条件。虽然存在许多成功地查明疾病相关模块的启发式方法，但基本的潜在连接模式在很大程度上仍未被探索。在这项工作中，我们的目标是通过分析70种复杂疾病的综合语料库的网络特性来填补这一空白。我们发现，疾病相关的蛋白质不存在于局部密集的社区，而是确定连接的重要性作为最具预测性的数量。这个数量启发了一种新的疾病模块检测（DIAMOnD）算法的设计，以识别一组已知疾病蛋白周围的完整疾病模块。我们使用控制良好的合成数据研究了算法的性能，并系统地验证了大型疾病语料库的识别邻域。疾病很少是单个基因异常的结果，而是涉及几个细胞过程之间的相互作用的整个级联。为了解开这些复杂的相互作用，有必要在蛋白质-蛋白质相互作用网络的背景下研究基因型-表型关系。我们对70种疾病的分析表明，疾病蛋白不是随机分散在这些网络中，而是聚集在特定区域，这表明每种疾病都存在特定的疾病模块。这些模块的识别是阐明疾病的生物学机制或有针对性地寻找药物靶点的第一步。我们提出了一个系统的疾病蛋白质的连接模式的分析，并确定最具预测性的拓扑特性，为他们的鉴定。这使我们能够合理地设计一个可靠和有效的疾病模块检测算法（DIAMOnD）。
The observation that disease associated proteins often interact with each other has fueled the development of network-based approaches to elucidate the molecular mechanisms of human disease. Such approaches build on the assumption that protein interaction networks can be viewed as maps in which diseases can be identified with localized perturbation within a certain neighborhood. The identification of these neighborhoods, or disease modules, is therefore a prerequisite of a detailed investigation of a particular pathophenotype. While numerous heuristic methods exist that successfully pinpoint disease associated modules, the basic underlying connectivity patterns remain largely unexplored. In this work we aim to fill this gap by analyzing the network properties of a comprehensive corpus of 70 complex diseases. We find that disease associated proteins do not reside within locally dense communities and instead identify connectivity significance as the most predictive quantity. This quantity inspires the design of a novel Disease Module Detection (DIAMOnD) algorithm to identify the full disease module around a set of known disease proteins. We study the performance of the algorithm using well-controlled synthetic data and systematically validate the identified neighborhoods for a large corpus of diseases. Diseases are rarely the result of an abnormality in a single gene, but involve a whole cascade of interactions between several cellular processes. To disentangle these complex interactions it is necessary to study genotype-phenotype relationships in the context of protein-protein interaction networks. Our analysis of 70 diseases shows that disease proteins are not randomly scattered within these networks, but agglomerate in specific regions, suggesting the existence of specific disease modules for each disease. The identification of these modules is the first step towards elucidating the biological mechanisms of a disease or for a targeted search of drug targets. We present a systematic analysis of the connectivity patterns of disease proteins and determine the most predictive topological property for their identification. This allows us to rationally design a reliable and efficient Disease Module Detection algorithm (DIAMOnD).
登录
查看更多内容
影响因子：
12.3
作者：
Hart GT;Ramani AK;Marcotte EM
通讯作者：
Marcotte EM
影响因子：
14.9
作者：
Hornbeck PV;Kornhauser JM;Tkachev S;Zhang B;Skrzypek E;Murray B;Latham V;Sullivan M
通讯作者：
Sullivan M
影响因子：
14.9
作者：
Ceol A;Chatr Aryamontri A;Licata L;Peluso D;Briganti L;Perfetto L;Castagnoli L;Cesareni G
通讯作者：
Cesareni G
DOI：
10.1073/pnas.0701722105
发表时间：
2008-03-18
影响因子：
11.1
作者：
Feldman, Igor;Rzhetsky, Andrey;Vitkup, Dennis
通讯作者：
Vitkup, Dennis
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne