The increasing number of peer-reviewed publications constitutes a challenge for biocuration. For example, NeuroMorpho.Org, a sharing platform for digital reconstructions of neural morphology, must evaluate more than 6000 potentially relevant articles per year to identify data of interest. Here, we describe a tool that uses natural language processing and deep learning to assess the likelihood of a publication to be relevant for the project. The tool automatically identifies articles describing digitally reconstructed neural morphologies with high accuracy. Its processing rate of 900 publications per hour is not only amply sufficient to autonomously track new research, but also allowed the successful evaluation of older publications backlogged due to limited human resources. The number of bio-entities found since launching the tool almost doubled while greatly reducing manual labor. The classification tool is open source, configurable, and simple to use, making it extensible to other biocuration projects.

中文翻译：

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自动识别描述神经形态数字重建的科学出版物

同行评审出版物数量的增加对生物管理构成了挑战。例如，神经形态数字重建共享平台 NeuroMorpho.Org 每年必须评估 6000 多篇潜在相关文章，以识别感兴趣的数据。在这里，我们描述了一种使用自然语言处理和深度学习来评估出版物与项目相关的可能性的工具。该工具自动识别高精度描述数字重建神经形态的文章。其每小时 900 份出版物的处理速度不仅足以自主跟踪新研究，而且还可以成功评估因人力资源有限而积压的旧出版物。自推出该工具以来，发现的生物实体数量几乎增加了一倍，同时大大减少了体力劳动。该分类工具是开源的、可配置的且易于使用，使其可以扩展到其他生物管理项目。