PubTator central: automated concept annotation for biomedical full text articles
PubTator central: automated concept annotation for biomedical full text articles
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DOI：
10.1093/nar/gkz389
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发表时间：
2019-07-02
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：
Wei, Chih-Hsuan;Allot, Alexis;Lu, Zhiyong
PubTator Central （https://www.ncbi.nlm.nih.gov/research/pubtator/）是一个web服务，用于查看和检索生物医学文章全文中的生物概念注释。PubTator Central （PTC）提供来自最先进的基因/蛋白质、遗传变异、疾病、化学物质、物种和细胞系的文本挖掘系统的自动注释，所有这些都可以立即下载。PTC注释PubMed（2900万篇摘要）和PMC文本挖掘子集（300万篇全文文章）。新的PTC web界面允许用户构建全文文档集合，并在每个文档中可视化概念注释。注释可以通过在线界面、RESTful web服务和批量FTP以多种格式（XML、JSON和tab分隔符）下载。改进的概念识别系统和基于深度学习的新的消歧模块提高了标注的准确性，并且新的服务器端架构明显更快。PTC与PubMed和PubMed Central同步，每天都有新的文章添加。最初的PubTator服务已经为大约3亿个请求提供了带注释的摘要，支持第三方研究用例，如生物定位支持、基因优先级、遗传疾病分析和基于文献的知识发现。我们在PTC中展示了全文结果，大大增加了生物医学概念的覆盖范围，并预计这种扩展将增强现有的下游应用并启用新的用例。
PubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for similar to 300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.