KDCS-PPI: Knowledge distillation with counterfactual sampling for Protein-Protein Interaction prediction

IF 7.5 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Bin Deng , Huifang Ma , Ruijia Zhang , Zhixin Li , Liang Chang
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Abstract

As the core of various biochemical reactions in life, Protein-Protein Interactions (PPIs) play a crucial role in maintaining the homeostasis of cellular functions, making the accurate prediction of PPIs particularly important. Traditional wet lab methods for predicting PPIs are time-consuming and costly. In contrast, PPI prediction methods utilizing Graph Neural Networks (GNNs) have exhibited promising performance and have increasingly emerged as the predominant approach in recent years. While GNNs rely on neighbor message aggregation, which can result in computational inefficiencies, Multilayer Perceptron (MLP) stand out for their time efficiency, as they do not require intricate handling of relational knowledge. However, MLPs often exhibit comparatively lower prediction accuracy. To leverage the advantages of both GNNs and MLPs in terms of effectiveness and efficiency, knowledge distillation techniques can be used to transfer the knowledge learned by GNNs to MLPs. During the knowledge distillation process, the knowledge transfer usually involves node feature embeddings rather than the interaction relationship knowledge between PPIs. Moreover, current methods frequently choose positive and negative samples for anchor nodes via random sampling, leading to suboptimal accuracy, especially for negative samples. To address this, we propose Knowledge Distillation with Counterfactual Sampling for Protein-Protein Interaction prediction (KDCS-PPI). Our method facilitates the transfer of diverse relational knowledge between proteins during the knowledge distillation process and utilizes a counterfactual sampling strategy to select more pertinent positive and negative examples. Extensive experiments on three datasets demonstrate that KDCS-PPI can be applied to large-scale PPI prediction tasks and achieves significant improvements in both effectiveness and computational efficiency compared to other benchmark methods. Our source codes will be publicly available at https://github.com/bin-db/KDCS-PPI.
KDCS-PPI:知识蒸馏与反事实采样的蛋白质-蛋白质相互作用预测
蛋白-蛋白相互作用(Protein-Protein interaction, PPIs)作为生命中各种生化反应的核心,对维持细胞功能的稳态起着至关重要的作用,因此准确预测PPIs就显得尤为重要。传统的湿实验室预测ppi的方法既耗时又昂贵。相比之下,利用图神经网络(gnn)的PPI预测方法表现出了良好的性能,近年来日益成为主流方法。虽然gnn依赖于邻居消息聚合,这可能导致计算效率低下,但多层感知器(MLP)因其时间效率而脱颖而出,因为它们不需要复杂的关系知识处理。然而,mlp往往表现出相对较低的预测精度。为了利用gnn和mlp在有效性和效率方面的优势,可以使用知识蒸馏技术将gnn学习到的知识转移到mlp中。在知识升华过程中,知识转移通常涉及节点特征嵌入,而不是ppi之间的交互关系知识。此外,目前的方法经常通过随机抽样来选择锚节点的正样本和负样本,导致准确性不理想,特别是对负样本。为了解决这个问题,我们提出了知识蒸馏与反事实采样用于蛋白质-蛋白质相互作用预测(KDCS-PPI)。我们的方法在知识蒸馏过程中促进了蛋白质之间各种关系知识的转移,并利用反事实采样策略来选择更相关的正反例。在三个数据集上的大量实验表明,与其他基准方法相比,KDCS-PPI可以应用于大规模PPI预测任务,并且在有效性和计算效率方面都有显著提高。我们的源代码将在https://github.com/bin-db/KDCS-PPI上公开提供。
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来源期刊
Expert Systems with Applications
Expert Systems with Applications 工程技术-工程:电子与电气
CiteScore
13.80
自引率
10.60%
发文量
2045
审稿时长
8.7 months
期刊介绍: Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.
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