Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Int J Mol Sci. 2024 May 27;25(11):5820. doi: 10.3390/ijms25115820.
Understanding protein-protein interactions (PPIs) helps to identify protein functions and develop other important applications such as drug preparation and protein-disease relationship identification. Deep-learning-based approaches are being intensely researched for PPI determination to reduce the cost and time of previous testing methods. In this work, we integrate deep learning with feature fusion, harnessing the strengths of both approaches, handcrafted features, and protein sequence embedding. The accuracies of the proposed model using five-fold cross-validation on Yeast core and Human datasets are 96.34% and 99.30%, respectively. In the task of predicting interactions in important PPI networks, our model correctly predicted all interactions in one-core, Wnt-related, and cancer-specific networks. The experimental results on cross-species datasets, including Caenorhabditis elegans, Helicobacter pylori, Homo sapiens, Mus musculus, and Escherichia coli, also show that our feature fusion method helps increase the generalization capability of the PPI prediction model.
理解蛋白质-蛋白质相互作用 (PPIs) 有助于确定蛋白质的功能，并开发其他重要的应用，如药物制备和蛋白质-疾病关系的识别。基于深度学习的方法被广泛研究用于确定 PPI，以降低以前测试方法的成本和时间。在这项工作中，我们将深度学习与特征融合相结合，利用这两种方法的优势，即手工制作的特征和蛋白质序列嵌入。在 Yeast core 和 Human 数据集上进行五重交叉验证时，所提出模型的准确率分别为 96.34%和 99.30%。在预测重要 PPI 网络中的相互作用任务中，我们的模型正确预测了一个核心、Wnt 相关和癌症特异性网络中的所有相互作用。在包括秀丽隐杆线虫、幽门螺杆菌、智人、小鼠和大肠杆菌在内的跨物种数据集上的实验结果也表明，我们的特征融合方法有助于提高 PPI 预测模型的泛化能力。
