Free Text Gene Name Recognition

1)自2005年以来，我一直是BioCreative研讨会的共同组织者，并参加了BioCreative II (2007), BioCreative III（2010）和BioCreative-2012研讨会（2012）。生物创意研讨会的总体目标是促进文本挖掘和文本处理工具的发展，这些工具对生物科学领域的研究人员和数据库管理员社区有用。我们对BioCreative III的贡献是组织基因规范化（GN）任务。这包括选择要注释的文档，监督注释过程，评估参与者提交的内容，编写任务的完整描述并在会议上展示结果。我们在任务中引入了两个创新。首先，我们使用了John Spouge和他的团队开发的Tap-k测量方法，并引入了EM算法来根据所有参与者的任务条目估计正确答案。Tap-k是一种性能度量，其最佳特征是截断的平均平均精度，并带有检索低于最后一次有用命中的无用记录的惩罚项。EM算法允许我们在更大的全文文档集上评估人们的预测，而不是为（507）提供黄金标准的人类判断。我们还为50个全文文档提供了人工注释。当EM算法评估的结果与金标准结果进行比较时，排名非常接近，并允许我们得出结论，自动评估方法成功地挑出了表现最好的系统。我们还进入了检测适合蛋白质相互作用的论文的分类任务。在这项任务中，我们使用优先级模型来识别基因/蛋白质名称，并使用解析来准备蛋白质与其他文本元素之间的依赖关系，并将这些关系以及文本单词作为特征。然后将机器学习应用于这个表示，我们在任务中提供了最佳性能。我们最近帮助组织了BioCreative-2012研讨会，该研讨会与bioocuration 2012会议有关，我们还参与了任务一，其中包括为CTD数据库创建一个分类系统。我们的方法基于BioCreative III用于GN任务的方法，但我们采用了基于语义分类器的不同方法来识别基因、蛋白质和疾病，并且我们还基于CTD数据库的LDA分析添加了特征。我们的方法在获得任务的最佳分类结果方面是有效的。

1) I have been a co-organizer of the BioCreative Workshops since 2005 and have taken part in BioCreative II (2007), BioCreative III (2010), and the BioCreative-2012 Workshop (2012). The overall goal of the BioCreative Workshops is to promote the development of text mining and text processing tools which are useful to the communities of researchers and database curators in the biological sciences. Our contribution for BioCreative III was to organize the Gene Normalization (GN) task. This included selecting documents to annotate, overseeing the annotation process, evaluating participants submissions, and writing up a full description of the task and presenting results at the conference. We introduced two innovations in the task. First, we used the Tap-k measure developed by John Spouge and his group and we introduced an EM algorithm to estimate the correct answers based on all participant entries for the task. Tap-k is a performance measure which can best be characterized as a truncated mean average precision with a penalty term for retrieving useless records below the last useful hit. The EM algorithm allowed us to evaluate peoples predictions over a much larger set of full text documents than we could provide gold standard human judgments for (507). We also provided a set of 50 full text documents for which we had human annotations. When the results of the EM algorithm evaluation were compared with the gold standard results the ranks were quite close and allowed us to conclude that the automatic method of assessment was successful in singling out the top performing systems. We also entered the triage task for detecting papers suitable for curation of protein-protein interactions. For this task we used the priority model to identify gene/protein names and used parsing to prepare dependency relations between proteins and other text elements and these relationships as well as text words were used as features. Machine learning was then applied to this representation and we turned in the best performance on the task. We recently helped organize the BioCreative-2012 Workshop associated with the Biocuration 2012 Conference and also participated in Task I which involved producing a triage system for the CTD database. Our approach was based on the approach used for the GN task of BioCreative III, but we took a different approach to identify genes, proteins, and diseases based on a semantic classifier and we also added features based on an LDA analysis of the CTD database. Our approach was effective in obtaining the best triage results on the task. 2) We recently co-chaired the BioCreative III Workshop in which the main competitive tasks were to find gene mentions in a full text article and map them to their GenBank identifiers and score them as to reliability, to classify PubMed records as likely to represent articles containing information on protein-protein interactions, and to find the text in full papers that describes the method used by an experimenter to experimentally verify a protein-protein interaction. We organized the first of these task and participated in the second. In the second task we used the priority model to locate protein mentions and it proved very successful and competitive with other approaches. 3) We are currently working to develop more general methods of finding high value articles for PPI based on their abstracts. This effort involves not only more powerful ranking methods, but also ways to display evidence to the user for a users quick evaluation.