Teaching and Research Office, Department of Automatic Control, College of Mechanical & Electronic Engineering and Automatization, National University of Defense Technology, Kaifu District, Changsha 410073, Hunan, China.
IEEE/ACM Trans Comput Biol Bioinform. 2013 Mar-Apr;10(2):514-21. doi: 10.1109/TCBB.2013.20.
The directionality of protein interactions is the prerequisite of forming various signaling networks, and the construction of signaling networks is a critical issue in the discovering the mechanism of the life process. In this paper, we proposed a novel method to infer the directionality in protein-protein interaction networks and furthermore construct signaling networks. Based on the functional annotations of proteins, we proposed a novel parameter GODS and established the prediction model. This method shows high sensitivity and specificity to predict the directionality of protein interactions, evaluated by fivefold cross validation. By taking the threshold value of GODS as 2, we achieved accuracy 95.56 percent and coverage 74.69 percent in the human test set. Also, this method was successfully applied to reconstruct the classical signaling pathways in human. This study not only provided an effective method to unravel the unknown signaling pathways, but also the deeper understanding for the signaling networks, from the aspect of protein function.
蛋白质相互作用的方向性是形成各种信号网络的前提，而信号网络的构建是发现生命过程机制的关键问题。在本文中，我们提出了一种新的方法来推断蛋白质相互作用网络中的方向性，并进一步构建信号网络。基于蛋白质的功能注释，我们提出了一个新的参数 GODS 并建立了预测模型。通过五重交叉验证评估，该方法在预测蛋白质相互作用的方向性方面表现出了高灵敏度和特异性。通过将 GODS 的阈值设置为 2，我们在人类测试集中实现了 95.56%的准确率和 74.69%的覆盖率。此外，该方法还成功地应用于重建人类经典信号通路。这项研究不仅提供了一种有效的方法来揭示未知的信号通路，而且从蛋白质功能的角度深入了解了信号网络。
