Named Entity Recognition and Relationship Extraction in Biomedicine

从生物医学文献中挖掘有用的知识有助于文献检索，自动化生物数据管理和许多其他科学任务。因此，重要的是能够识别自由文本中的各种类型的生物实体，例如基因/蛋白质，疾病/病症和药物/化学品等。事实上，我们以前的PubMed日志分析显示，人们搜索某些生物医学概念的频率高于其他概念，并且不同概念之间存在很强的关联。例如，疾病名称通常与基因/蛋白质和药物名称共同出现。我们最近的研究引入了一种名为DNorm的机器学习方法，用于基于成对学习排名的归一化。在2013-2014年，我们继续努力通过基于低秩矩阵近似的降维技术来提高DNorm的可扩展性。在我们最近开发的NCBI疾病语料库上进行评估时，新算法显示出要学习的参数数量显着减少，同时保持高准确性。 除了疾病命名实体识别（NER），我们还改进了化学NER问题的最新技术。通过BioCreative IV CHEMDNER任务的参与，我们介绍了tmChem系统，这是一种通过将两个独立的机器学习模型组合在一起而创建的化学命名实体识别器。我们使用挑战任务语料库来开发和评估tmChem，在提及级评估中实现了0.8739的微平均f测量，这是所有参与团队中最高的表现。 为了提高我们团队多年来创建的各种生物医学文本挖掘工具（例如DNorm，tmChem，tmVar等）之间的互操作性，我们最近采用了一个新提出的社区范围内的计划，称为BioC，用于处理数据格式的异质性和多样性。具体来说，我们修改了我们的工具，使它们能够以建议的BioC格式读取/写入数据。最终的BioC包装工具包被命名为tmBioC。通过实证研究，我们证明了我们在tmBioC中的工具可以更有效地相互集成以及与外部工具集成：我们的实验结果表明，使用BioC减少了>60%的文本挖掘工具集成代码行。 如前所述，文本挖掘研究的一个很有前途的应用领域是帮助手动文献管理，这是一个非常耗时和劳动密集型的过程。在这方面，我们专注于基因本体（GO）注释，这是一个共同的任务之间的模式生物数据库（MOD）组。这也是一项非常耗时和劳动密集型的任务，因此经常被认为是文献策展的瓶颈之一。人们越来越需要半自动或全自动的GO策展技术，以帮助数据库管理员快速准确地识别全长文章中的基因功能信息。尽管在过去进行了多次尝试，但很少有研究被证明在帮助现实世界的GO策展方面是有用的。由于缺乏高级训练数据以及文本挖掘开发人员和GO管理员之间的互动机会，限制了算法开发的进展以及在实际情况中的相应使用。为此，我们在BioCreative IV中组织了一个基于文献的GO注释的文本挖掘挑战任务。更具体地说，我们开发了两个子任务：a）自动定位包含GO相关信息的文本段落（文本检索任务）和B）自动识别给定文章中基因的相关GO术语（概念识别任务）。在五个MOD的支持下，我们为团队提供了近4，000个独特的文本段落，这些段落作为我们任务数据中每个GO注释的基础。这种证据文本信息长期以来一直被认为是文本挖掘算法开发的关键，但由于管理成本高而从未提供。总共有7支队伍参加了挑战任务。从团队的结果中，我们发现，与过去类似的任务结果相比，识别GO术语的性能得到了全面改善。未来的工作应该集中在提高性能的GO概念识别，并将文本挖掘工具的实际好处到现实世界的GO注释。

Mining useful knowledge from the biomedical literature holds potentials for helping literature searching, automating biological data curation and many other scientific tasks. Hence, it is important to be able to recognize various types of biological entities in free text, such as gene/proteins, disease/conditions, and drug/chemicals, etc. Indeed, our previous PubMed log analysis revealed that people search certain biomedical concepts more often than others and that there exist strong associations between different concepts. For example, a disease name often co-occurs with gene/proteins and drug names. Our recent research introduced a machine learning method called DNorm for normalization based on pairwise learning to rank. In 2013-2014, we have continued our efforts in improving DNorm by increasing its scalability through a dimension reduction technique based on low-rank matrix approximation. When assessed on our recently developed NCBI disease corpus, the new algorithm demonstrates a significant reduction in the number of parameters to be learned while maintaining a high accuracy. Besides disease named entity recognition (NER), we also improved the state of the art in the chemical NER problem. Through participation of the BioCreative IV CHEMDNER task, we introduced the tmChem system, a chemical named entity recognizer created by combining two independent machine-learning models in an ensemble. We used the challenge task corpus to develop and evaluate tmChem, achieving a micro-averaged f-measure of 0.8739 on the mention-level evaluation, which is the highest performance among all participating teams. To improve the interoperability among various biomedical text-mining tools our group has created over the years (e.g. DNorm, tmChem, tmVar, etc), we recently adopted a newly proposed community-wide scheme called BioC for handling heterogeneity and variety in data formats. Specifically, we modified our tools and enabled them to read/write data in the proposed BioC format. The resulting BioC wrapped toolkit, is named as tmBioC. Through empirical studies, we demonstrated that our tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. As mentioned earlier, one promising application area for text mining research is to assist manual literature curation, a highly time-consuming and labor-intensive process. In this regard, we focused on Gene Ontology (GO) annotation, which is a common task among model organism database (MOD) groups. It is also a very time-consuming and labor-intensive task, thus often considered as one of the bottlenecks in literature curation. There is a growing need for semi- or fully-automated GO curation techniques that will help database curators rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The lack of sentence-level training data and opportunities for interaction between text mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two sub-tasks: a) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and b) to automatically identify relevant GO terms for the genes in a given article (a concept recognition task). With the support from five MODs, we provided teams with nearly 4,000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available due to the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we find an overall improvement in performance for recognizing GO terms when comparing to similar task results in the past. Future work should focus on improving performance of GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation.