Goetz Thomas, von der Lieth Claus-Wilhelm
Central Spectroscopy Department (B090)-Molecular, Modeling, German Cancer Research Center, Heidelberg Im Neuenheimer Feld 280, D-69120 Heidelberg, Germany.
Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W774-8. doi: 10.1093/nar/gki429.
Since it is becoming increasingly laborious to manually extract useful information embedded in the ever-growing volumes of literature, automated intelligent text analysis tools are becoming more and more essential to assist in this task. PubFinder (www.glycosciences.de/tools/PubFinder) is a publicly available web tool designed to improve the retrieval rate of scientific abstracts relevant for a specific scientific topic. Only the selection of a representative set of abstracts is required, which are central for a scientific topic. No special knowledge concerning the query-syntax is necessary. Based on the selected abstracts, a list of discriminating words is automatically calculated, which is subsequently used for scoring all defined PubMed abstracts for their probability of belonging to the defined scientific topic. This results in a hit-list of references in the descending order of their likelihood score. The algorithms and procedures implemented in PubFinder facilitate the perpetual task for every scientist of staying up-to-date with current publications dealing with a specific subject in biomedicine.
由于从不断增加的文献中手动提取有用信息变得越来越费力，自动化智能文本分析工具对于协助完成这项任务变得越来越重要。PubFinder（www.glycosciences.de/tools/PubFinder）是一个公开可用的网络工具，旨在提高与特定科学主题相关的科学摘要的检索率。只需要选择一组具有代表性的摘要，这些摘要对于一个科学主题至关重要。不需要有关查询语法的特殊知识。基于所选摘要，会自动计算出一组区分性词汇，随后用于对所有定义的PubMed摘要属于所定义科学主题的概率进行评分。这会生成一个按可能性得分降序排列的参考文献命中列表。PubFinder中实施的算法和程序有助于每位科学家持续跟进处理生物医学特定主题的当前出版物这一长期任务。
