Knowledge about the relations between biomedical entities (such as drugs and targets) is widely distributed in more than 30 million research articles and consistently plays an important role in the development of biomedical science. In this work, we propose a novel machine learning framework, named BERE, for automatically extracting biomedical relations from large-scale literature repositories. BERE uses a hybrid encoding network to better represent each sentence from both semantic and syntactic aspects, and employs a feature aggregation network to make predictions after considering all relevant statements. More importantly, BERE can also be trained without any human annotation via a distant supervision technique. Through extensive tests, BERE has demonstrated promising performance in extracting biomedical relations, and can also find meaningful relations that were not reported in existing databases, thus providing useful hints to guide wet-lab experiments and advance the biological knowledge discovery process.

关于生物医学实体（例如药物和靶标）之间的关系的知识广泛分布在三千万篇研究论文中，并且始终在生物医学科学的发展中发挥重要作用。在这项工作中，我们提出了一种新颖的机器学习框架，称为BERE，用于自动从大规模文献库中提取生物医学关系。BERE使用混合编码网络从语义和句法方面更好地表示每个句子，并在考虑所有相关语句之后采用特征聚合网络进行预测。更重要的是，还可以通过远程监控技术对BERE进行培训，而无需任何人工注释。通过广泛的测试，BERE在提取生物医学关系方面显示出令人鼓舞的性能，