Table_2_Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network.XLS

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.近年来，一系列基于蛋白质-蛋白质相互作用（protein-protein interaction, PPI）网络的计算模型相继被提出。然而，由于假阳性、假阴性以及PPI网络的不完整性，在推断关键蛋白质时，构建具备令人满意预测精度的计算模型仍面临诸多挑战。本研究提出了一种名为WPDINM的预测模型，用于基于新型加权蛋白质结构域相互作用（protein-domain interaction, PDI）网络检测关键蛋白质。在WPDINM模型中，首先将蛋白质的基因表达数据与从原始PPI网络中提取的拓扑信息相结合，构建加权PPI网络；同时，基于原始PDI网络构建加权结构域-结构域相互作用（domain-domain interaction, DDI）网络。随后，通过将新获得的加权PPI网络、加权DDI网络与原始PDI网络进行整合，进一步构建加权PDI网络。接着，基于拓扑特征以及包含蛋白质亚细胞定位和直系同源信息在内的生物学信息，设计了一种基于PageRank的新型迭代算法，并在新构建的加权PDI网络上运行该算法以评估蛋白质的关键性。最后，为评估WPDINM的预测性能，我们将其与12种竞争性方法进行了对比。实验结果显示，WPDINM在Top 1%、Top 5%、Top 10%、Top 15%、Top 20%以及Top 25%的排名区间中，分别可达到90.19%、81.96%、70.72%、62.04%、55.83%和51.13%的预测准确率，其预测精度超越了传统当前最优的同类对比方法。凭借优异的识别效果，WPDINM方法有望为关键蛋白质识别领域的进一步发展提供助力。