Modern biology produces data at a staggering rate. Yet, much of these biological data is still isolated in the text, figures, tables and supplementary materials of articles. As a result, biological information created at great expense is significantly underutilised. The protein motif biology field does not have sufficient resources to curate the corpus of motif-related literature and, to date, only a fraction of the available articles have been curated. In this study, we develop a set of tools and a web resource, ‘articles.ELM’, to rapidly identify the motif literature articles pertinent to a researcher’s interest. At the core of the resource is a manually curated set of about 8000 motif-related articles. These articles are automatically annotated with a range of relevant biological data allowing in-depth search functionality. Machine-learning article classification is used to group articles based on their similarity to manually curated motif classes in the Eukaryotic Linear Motif resource. Articles can also be manually classified within the resource. The ‘articles.ELM’ resource permits the rapid and accurate discovery of relevant motif articles thereby improving the visibility of motif literature and simplifying the recovery of valuable biological insights sequestered within scientific articles. Consequently, this web resource removes a critical bottleneck in scientific productivity for the motif biology field. Database URL: http://slim.icr.ac.uk/articles/

中文翻译：

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ELM资源：通过注释，文本挖掘和分类简化对蛋白质线性基序文献的访问。

现代生物学以惊人的速度产生数据。然而，许多生物学数据仍然孤立在文章的文本，图形，表格和补充材料中。结果，大量花费的生物信息被充分利用。蛋白质基序生物学领域没有足够的资源来策划与基序相关的文献的语料库，并且迄今为止，只有一小部分可用的文章已经被策划。在这项研究中，我们开发了一套工具和网络资源“ articles.ELM”，以快速识别与研究人员的兴趣相关的主题文献文章。该资源的核心是一组人工策划的约8000个与主题相关的文章。这些文章会自动用一系列相关的生物学数据进行注释，从而提供了深入的搜索功能。机器学习文章分类用于根据文章与真核线性母题资源中手动策划的主题类的相似性对文章进行分组。文章也可以在资源中手动分类。“ articles.ELM”资源允许快速准确地发现相关的主题文章，从而提高主题文献的知名度，并简化对科学文章中保存的宝贵生物学见解的回收。因此，此Web资源消除了主题生物学领域在科学生产率方面的关键瓶颈。数据库网址：http://slim.icr.ac.uk/articles/ “ articles.ELM”资源允许快速准确地发现相关的主题文章，从而提高主题文献的知名度，并简化对科学文章中保存的宝贵生物学见解的回收。因此，此Web资源消除了主题生物学领域在科学生产率方面的关键瓶颈。数据库网址：http://slim.icr.ac.uk/articles/ “ articles.ELM”资源允许快速准确地发现相关的主题文章，从而提高主题文献的知名度，并简化对科学文章中保存的宝贵生物学见解的回收。因此，此Web资源消除了主题生物学领域科学生产率的关键瓶颈。数据库网址：http://slim.icr.ac.uk/articles/