生物医学关系预测的知识图嵌入

Xiaohan Qu, Yongming Cai
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引用次数: 0

摘要

生物医学关系分类旨在实现生物医学关系的自动化检测和分类,这对各种生物医学研究和应用具有很大的优势。随着机器学习的发展,基于计算模型的方法已被应用于生物医学关系分类,并在一些公共数据集和共享任务上取得了最先进的性能。然而,现有的模型在表达大型知识图的特征方面存在一定的局限性。例如,多层知识图嵌入(Knowledge Graph Embedding, KGE)网络结构具有完全连接的层,容易出现过拟合。受多层卷积网络模型ConvE的启发,本文提出了一种新的用于生物医学关系分类的KGE模型ConvE- bio。新模型在DDI(药物-药物相互作用)、DTI(药物-靶标相互作用)和PPI(蛋白质-蛋白质相互作用)数据集上表现良好,优于经典基线算法。结果表明,ConvE-Bio可作为生物医学关系分类领域的有力工具,用于药物开发、多药副作用预测等研究。
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ConvE-Bio: Knowledge Graph Embedding for Biomedical Relation Prediction
Biomedical relation classification aims to automate the detection and classification of biomedical relationships, which has great advantages for various biomedical research and applications. With the development of machine learning, computational model-based approaches have been applied to biomedical relation classification and achieved state-of-the-art performance on some public datasets and shared tasks. Nevertheless, the existing models have some limitations in expressing features of large knowledge graphs. For example, the multilayer Knowledge Graph Embedding (KGE) network structure has fully connected layers and is prone to overfitting. Inspired by the multi-layer convolutional network model ConvE, this paper proposes a novel KGE model named ConvE-Bio for biomedical relation classification. The novel model performs well on the DDI (Drug-Drug Interaction), DTI (Drug-Target Interaction), and PPI (Protein-Protein Interaction) datasets, outperforming the classical baseline algorithms. Results show that ConvE-Bio can be used as a powerful tool in the field of biomedical relation classification for drug development, polypharmacy side-effect prediction and other research.
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