School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China.
School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China.
J Biomed Inform. 2019 Nov;99:103294. doi: 10.1016/j.jbi.2019.103294. Epub 2019 Sep 23.
The explosive growth of biomedical literature has created a rich source of knowledge, such as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in unstructured free text. Biomedical relation classification aims to automatically detect and classify biomedical relations, which has great benefits for various biomedical research and applications. In the past decade, significant progress has been made in biomedical relation classification. With the advance of neural network methodology, neural network-based approaches have been applied in biomedical relation classification and achieved state-of-the-art performance for some public datasets and shared tasks. In this review, we describe the recent advancement of neural network-based approaches for classifying biomedical relations. We summarize the available corpora and introduce evaluation metrics. We present the general framework for neural network-based approaches in biomedical relation extraction and pretrained word embedding resources. We discuss neural network-based approaches, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We conclude by describing the remaining challenges and outlining future directions.
生物医学文献的爆炸式增长产生了丰富的知识来源，例如蛋白质-蛋白质相互作用 (PPIs) 和药物-药物相互作用 (DDIs)，这些知识都以非结构化的自由文本形式存在。生物医学关系分类旨在自动检测和分类生物医学关系，这对各种生物医学研究和应用都有很大的好处。在过去的十年中，生物医学关系分类取得了重大进展。随着神经网络方法的进步，基于神经网络的方法已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。在这篇综述中，我们描述了基于神经网络的生物医学关系分类方法的最新进展。我们总结了现有的语料库并介绍了评估指标。我们提出了基于神经网络的生物医学关系提取的一般框架和预训练的单词嵌入资源。我们讨论了基于神经网络的方法，包括卷积神经网络 (CNNs) 和循环神经网络 (RNNs)。最后，我们描述了剩余的挑战并概述了未来的方向。
