Membrane proteins are gatekeepers to the cell and essential for determination of the function of cells. Identification of the types of membrane proteins is an essential problem in cell biology. It is time-consuming and expensive to identify the type of membrane proteins with traditional experimental methods. The alternative way is to design effective computational methods, which can provide quick and reliable predictions. To date, several computational methods have been proposed in this regard. Several of them used the features extracted from the sequence information of individual proteins. Recently, networks are more and more popular to tackle different protein-related problems, which can organize proteins in a system level and give an overview of all proteins. However, such form weakens the essential properties of proteins, such as their sequence information. In this study, a novel feature fusion scheme was proposed, which integrated the information of protein sequences and protein-protein interaction network. The fused features of a protein were defined as the linear combination of sequence features of all proteins in the network, where the combination coefficients were the probabilities yielded by the random walk with restart algorithm with the protein as the seed node. Several models with such fused features and different classification algorithms were built and evaluated. Their performance for predicting the type of membrane proteins was improved compared with the models only with the sequence features or network information.

膜蛋白是细胞的守门员，对确定细胞功能至关重要。膜蛋白类型的鉴定是细胞生物学中的基本问题。用传统的实验方法鉴定膜蛋白的类型既费时又昂贵。另一种方法是设计有效的计算方法，该方法可以提供快速而可靠的预测。迄今为止，在这方面已经提出了几种计算方法。他们中的一些人使用了从单个蛋白质的序列信息中提取的特征。最近，网络越来越多地用于解决与蛋白质相关的各种不同问题，这些问题可以在系统级别组织蛋白质并提供所有蛋白质的概述。但是，这种形式会削弱蛋白质的基本特性，例如其序列信息。在这项研究中，提出了一种新颖的特征融合方案，该方案融合了蛋白质序列信息和蛋白质-蛋白质相互作用网络。蛋白质的融合特征定义为网络中所有蛋白质的序列特征的线性组合，其中组合系数为以蛋白质为种子节点的重启随机游走产生的概率。建立并评估了具有这种融合特征和不同分类算法的几个模型。与仅具有序列特征或网络信息的模型相比，它们预测膜蛋白类型的性能有所提高。整合了蛋白质序列信息和蛋白质-蛋白质相互作用网络。蛋白质的融合特征定义为网络中所有蛋白质的序列特征的线性组合，其中组合系数为以蛋白质为种子节点的重启随机游走产生的概率。建立并评估了具有这种融合特征和不同分类算法的几个模型。与仅具有序列特征或网络信息的模型相比，它们预测膜蛋白类型的性能有所提高。整合了蛋白质序列信息和蛋白质-蛋白质相互作用网络。蛋白质的融合特征定义为网络中所有蛋白质的序列特征的线性组合，其中组合系数为以蛋白质为种子节点的重启随机游走产生的概率。建立并评估了具有这种融合特征和不同分类算法的几个模型。与仅具有序列特征或网络信息的模型相比，它们预测膜蛋白类型的性能有所提高。其中组合系数是通过以蛋白质为种子节点的重启算法进行的随机游走所产生的概率。建立并评估了具有这种融合特征和不同分类算法的几个模型。与仅具有序列特征或网络信息的模型相比，它们预测膜蛋白类型的性能有所提高。其中组合系数是通过以蛋白质为种子节点的重启算法进行的随机游走所产生的概率。建立并评估了具有这种融合特征和不同分类算法的几个模型。与仅具有序列特征或网络信息的模型相比，它们预测膜蛋白类型的性能有所提高。