Towards an Integrated Protein-Protein Interaction Network
Towards an Integrated Protein-Protein Interaction Network
复制标题
迈向整合的蛋白质-蛋白质相互作用网络
DOI：
10.1007/11415770_2
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
N. Friedman
中科院分区：
文献类型：
--
作者：
A. Jaimovich;G. Elidan;H. Margalit;N. Friedman
蛋白质 - 蛋白质相互作用在大多数细胞过程中起主要作用。因此，识别细胞中相互作用蛋白质的全部曲目的挑战非常重要，并且在实验和计算上已被解决。如今，蛋白质相互作用的大规模实验研究虽然部分和嘈杂，但使我们能够表征相互作用蛋白质的特性并开发预测算法。但是，大多数现有的算法忽略了相互作用对之间可能的依赖性，而是彼此独立地预测它们。在这项研究中，我们提出了一种计算方法，该方法通过同时预测蛋白质 - 蛋白质相互作用来克服这一缺点。此外，我们的方法使我们能够整合各种蛋白质属性，并明确说明测定测量值的不确定性。使用关系马尔可夫网络的语言，我们构建了一个包括所有这些元素的统一概率模型。我们展示了如何学习模型属性，然后使用它来同时预测所有未观察到的交互。我们的结果表明，通过对相互作用之间的依赖性进行建模，以及考虑到蛋白质属性和测量噪声，我们可以对蛋白质相互作用网络进行更准确的描述。此外，我们的方法使我们能够对相互作用蛋白质的特性获得新的见解。
Protein-protein interactions play a major role in most cellular processes. Thus, the challenge of identifying the full repertoire of interacting proteins in the cell is of great importance and has been addressed both experimentally and computationally. Today, large scale experimental studies of protein interactions, while partial and noisy, allow us to characterize properties of interacting proteins and develop predictive algorithms. Most existing algorithms, however, ignore possible dependencies between interacting pairs and predict them independently of one another. In this study, we present a computational approach that overcomes this drawback by predicting protein-protein interactions simultaneously. In addition, our approach allows us to integrate various protein attributes and explicitly account for uncertainty of assay measurements. Using the language of relational Markov networks, we build a unified probabilistic model that includes all of these elements. We show how we can learn our model properties and then use it to predict all unobserved interactions simultaneously. Our results show that by modeling dependencies between interactions, as well as by taking into account protein attributes and measurement noise, we achieve a more accurate description of the protein interaction network. Furthermore, our approach allows us to gain new insights into the properties of interacting proteins.
影响因子：
10.5
作者：
Kumar, A;Agarwal, S;Snyder, M
通讯作者：
Snyder, M