Knowledge-driven geospatial location resolution for phylogeographic models of virus migration.
Knowledge-driven geospatial location resolution for phylogeographic models of virus migration.
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DOI：
10.1093/bioinformatics/btv259
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发表时间：
2015-06-15
期刊：
影响因子：
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通讯作者：
Gonzalez G
中科院分区：
文献类型：
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作者：
Weissenbacher D;Tahsin T;Beard R;Figaro M;Rivera R;Scotch M;Gonzalez G
摘要：人畜共患病毒（可在人与动物之间传播的病毒）引起的疾病是全世界公共卫生的主要威胁。通过对病毒迁移和突变模式的研究，系统地理学领域为改进其监测提供了有价值的工具。人畜共患病毒系统地理学分析的一个关键组成部分包括确定相关病毒序列的特定位置。这通常通过查询公共数据库（如GenBank）和检查记录中的地理空间元数据来完成。当没有足够的细节时，合乎逻辑的下一步是研究人员对相应发表的文章进行手工调查。动机：在本文中，我们提出了一种在全文文章中检测和消除位置歧义（地名解析）的系统，以自动检索足够的元数据。我们的系统已经在人工注释的与物种地理学相关的期刊文章语料库上进行了测试，使用集成启发式方法进行位置消歧，包括距离启发式，种群启发式和利用从GenBank元数据获得的知识的新启发式（即“元数据启发式”）。结果：对于位置的检测和消歧，我们的系统使用元数据启发式表现最好（精度0.54，召回率0.89,F-score 0.68）。仅检查位置名称消歧时，精度达到0.88。我们的误差分析表明，通过改进地理空间位置检测，可以显著提高地名分辨率的准确性。通过改进这些基本的自动化任务，我们的系统可以成为依赖GenBank序列地理空间元数据的系统地理学家的有用资源。联系人:davy.weissenbacher@asu.edu
Summary: Diseases caused by zoonotic viruses (viruses transmittable between humans and animals) are a major threat to public health throughout the world. By studying virus migration and mutation patterns, the field of phylogeography provides a valuable tool for improving their surveillance. A key component in phylogeographic analysis of zoonotic viruses involves identifying the specific locations of relevant viral sequences. This is usually accomplished by querying public databases such as GenBank and examining the geospatial metadata in the record. When sufficient detail is not available, a logical next step is for the researcher to conduct a manual survey of the corresponding published articles. Motivation: In this article, we present a system for detection and disambiguation of locations (toponym resolution) in full-text articles to automate the retrieval of sufficient metadata. Our system has been tested on a manually annotated corpus of journal articles related to phylogeography using integrated heuristics for location disambiguation including a distance heuristic, a population heuristic and a novel heuristic utilizing knowledge obtained from GenBank metadata (i.e. a ‘metadata heuristic’). Results: For detecting and disambiguating locations, our system performed best using the metadata heuristic (0.54 Precision, 0.89 Recall and 0.68 F-score). Precision reaches 0.88 when examining only the disambiguation of location names. Our error analysis showed that a noticeable increase in the accuracy of toponym resolution is possible by improving the geospatial location detection. By improving these fundamental automated tasks, our system can be a useful resource to phylogeographers that rely on geospatial metadata of GenBank sequences. Contact: davy.weissenbacher@asu.edu