Klinger Roman, Friedrich Christoph M, Mevissen Heinz Theodor, Fluck Juliane, Hofmann-Apitius Martin, Furlong Laura I, Sanz Ferran
Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Schloss Birlinghoven, 53754 Sankt Augustin, Germany.
J Bioinform Comput Biol. 2007 Dec;5(6):1277-96. doi: 10.1142/s0219720007003156.
The influence of genetic variations on diseases or cellular processes is the main focus of many investigations, and results of biomedical studies are often only accessible through scientific publications. Automatic extraction of this information requires recognition of the gene names and the accompanying allelic variant information. In a previous work, the OSIRIS system for the detection of allelic variation in text based on a query expansion approach was communicated. Challenges associated with this system are the relatively low recall for variation mentions and gene name recognition. To tackle this challenge, we integrate the ProMiner system developed for the recognition and normalization of gene and protein names with a conditional random field (CRF)-based recognition of variation terms in biomedical text. Following the newly developed normalization of variation entities, we can link textual entities to Single Nucleotide Polymorphism database (dbSNP) entries. The performance of this novel approach is evaluated, and improved results in comparison to state-of-the-art systems are reported.
基因变异对疾病或细胞过程的影响是许多研究的主要焦点，生物医学研究的结果通常只能通过科学出版物获取。自动提取这些信息需要识别基因名称和相关的等位基因变异信息。在之前的一项工作中，介绍了基于查询扩展方法的用于检测文本中等位基因变异的OSIRIS系统。与该系统相关的挑战是变异提及和基因名称识别的召回率相对较低。为应对这一挑战，我们将为识别和标准化基因及蛋白质名称而开发的ProMiner系统与基于条件随机场（CRF）的生物医学文本变异术语识别相结合。根据新开发的变异实体标准化方法，我们可以将文本实体链接到单核苷酸多态性数据库（dbSNP）条目。对这种新方法的性能进行了评估，并报告了与现有系统相比有所改进的结果。
