Extracting causal relations on HIV drug resistance from literature
Extracting causal relations on HIV drug resistance from literature
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DOI：
10.1186/1471-2105-11-101
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发表时间：
2010-02-23
影响因子：
3
通讯作者：
Sloot, Peter M. A.
中科院分区：
文献类型：
--
作者：
Bui, Quoc-Chinh;Nuallain, Breanndan O.;Sloot, Peter M. A.
背景：在艾滋病毒治疗中，掌握适用药物的最新耐药性数据至关重要，因为艾滋病毒具有非常高的突变率。这些数据通过科学出版物提供，必须由专家手动提取，以便供病毒学家和医生使用。因此，迫切需要一种工具，部分自动化这一过程，并能够检索药物和病毒突变之间的关系从literation.Results：在这项工作中，我们提出了一种新的方法来提取和联合收割机之间的关系HIV药物和病毒基因组中的突变。我们的提取方法是基于自然语言处理（NLP）产生的语法关系，并适用于这些关系的规则集。我们将我们的方法应用于一组相关的PubMed摘要，并获得了2，434个提取的关系，F分数的估计性能为84%。然后，我们使用逻辑回归组合提取的关系，以生成每对的电阻值。这种关系组合的结果显示，与斯坦福大学的HIV数据库的10个最常见的突变超过85%的一致性。该系统是用于在5家医院从Virolab项目http://www.virolab.org预选最相关的新的耐药数据，从文献和目前的病毒学家和医生作进一步evaluation.Conclusions：所提出的关系提取和组合方法具有良好的性能提取艾滋病毒耐药数据。它可以用于大规模的关系抽取实验。所开发的方法也可以应用于提取其他类型的关系，如基因蛋白质，基因疾病，疾病突变。
Background: In HIV treatment it is critical to have up-to-date resistance data of applicable drugs since HIV has a very high rate of mutation. These data are made available through scientific publications and must be extracted manually by experts in order to be used by virologists and medical doctors. Therefore there is an urgent need for a tool that partially automates this process and is able to retrieve relations between drugs and virus mutations from literature.Results: In this work we present a novel method to extract and combine relationships between HIV drugs and mutations in viral genomes. Our extraction method is based on natural language processing (NLP) which produces grammatical relations and applies a set of rules to these relations. We applied our method to a relevant set of PubMed abstracts and obtained 2,434 extracted relations with an estimated performance of 84% for F-score. We then combined the extracted relations using logistic regression to generate resistance values for each pair. The results of this relation combination show more than 85% agreement with the Stanford HIVDB for the ten most frequently occurring mutations. The system is used in 5 hospitals from the Virolab project http://www.virolab.org to preselect the most relevant novel resistance data from literature and present those to virologists and medical doctors for further evaluation.Conclusions: The proposed relation extraction and combination method has a good performance on extracting HIV drug resistance data. It can be used in large-scale relation extraction experiments. The developed methods can also be applied to extract other type of relations such as gene-protein, gene-disease, and disease-mutation.