LitSuggest: a web-based system for literature recommendation and curation using machine learning
LitSuggest: a web-based system for literature recommendation and curation using machine learning
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DOI：
10.1093/nar/gkab326
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发表时间：
2021-05-05
影响因子：
14.9
通讯作者：
Lu, Zhiyong
中科院分区：
文献类型：
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作者：
Allot, Alexis;Lee, Kyubum;Lu, Zhiyong
检索和阅读相关文献是生物医学研究中的常规做法。然而，它是具有挑战性的用户设计最佳的搜索查询使用所有的关键字相关的一个给定的主题。因此，现有的搜索系统，如PubMed，往往返回次优的结果。文献推荐中的几种计算方法被提出来作为基于关键词的查询方法的有效替代。然而，这些方法需要机器学习和自然语言处理方面的专业知识，这可能使生物学家难以利用它们。在本文中，我们提出了LitSuggest，一个Web服务器，提供了一个全功能的文献推荐和策展服务，以帮助生物医学研究人员保持最新的科学文献。LitSuggest结合了先进的机器学习技术，以高准确度推荐相关的PubMed文章。除了创新的文本处理方法外，LitSuggest还提供了比现有工具更多的优势。首先，LitSuggest允许用户在单个界面中策划、组织和下载分类结果。其次，用户可以通过更新训练语料库来轻松微调LitSuggest结果。第三，成果可以很容易地共享，从而实现科学文献的合作分析和管理。最后，LitSuggest为每个用户的项目提供了一个自动化的个性化的每周新发表文章摘要。
Searching and reading relevant literature is a routine practice in biomedical research. However, it is challenging for a user to design optimal search queries using all the keywords related to a given topic. As such, existing search systems such as PubMed often return suboptimal results. Several computational methods have been proposed as an effective alternative to keyword-based query methods for literature recommendation. However, those methods require specialized knowledge in machine learning and natural language processing, which can make them difficult for biologists to utilize. In this paper, we propose LitSuggest, a web server that provides an all-in-one literature recommendation and curation service to help biomedical researchers stay up to date with scientific literature. LitSuggest combines advanced machine learning techniques for suggesting relevant PubMed articles with high accuracy. In addition to innovative text-processing methods, LitSuggest offers multiple advantages over existing tools. First, LitSuggest allows users to curate, organize, and download classification results in a single interface. Second, users can easily fine-tune LitSuggest results by updating the training corpus. Third, results can be readily shared, enabling collaborative analysis and curation of scientific literature. Finally, LitSuggest provides an automated personalized weekly digest of newly published articles for each user's project.