SEI+II(BIO): BioTagger - Biological Entity Tagging Using Online Resources and Machine Learning

SEI II(BIO)：BioTagger - 使用在线资源和机器学习进行生物实体标记

• 批准号: 0430743
• 负责人: Hongfang Liu
• 金额: $ 82.31万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2006-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0430743&HistoricalAwards=false
• 关键词: SEI+II; BIO; BioTagger; Biological; Entity

As the pace of biological research increases, computers are being used to manage the explosive amount of biological information. Much of information relevant to biological research is recorded either as coded data in biological databases or as free text in journal articles and in annotation fields of biological databases. Natural language processing tools have shown to have the potential to decrease the difficulty of managing information in biomedical free text.This project aims to use online resources (e.g., genetic databases, free-text corpora or machine readable dictionaries) and machine learning techniques for the construction of a biological entity tagging system that associates terms mentioned in text with entries in databases. Biological entity tagging is extremely challenging because of novelty, synonymy and ambiguity associated with terms representing biological entities in text. The project includes the construction of a biological entity dictionary and the acquisition of disambiguation knowledge using online resources. It also includes the development of dictionary lookup method and the employment of machine learning techniques for resolving ambiguity, discovering novelty, and recognizing synonymy. The research will generate several deliverables and the enriched information on gene/protein names, bibliography, and other annotation fields will be integrated into UniProt/PIR databases, which is an ongoing international effort on protein databases.The project provides an opportunity of furthering the collaborations among Columbia University, Georgetown University Medical Center and University of Maryland at Baltimore County. The project also integrates educational and research activities by having graduate and undergraduate students involved in the overall project.

随着生物学研究步伐的加快，计算机正被用来管理爆炸性数量的生物信息。许多与生物学研究相关的信息要么以编码数据的形式记录在生物学数据库中，要么以自由文本的形式记录在期刊文章和生物学数据库的注释字段中。自然语言处理工具已被证明具有降低生物医学自由文本中信息管理难度的潜力。该项目旨在使用在线资源（例如，遗传数据库、自由文本语料库或机器可读词典）和用于构建生物实体标记系统的机器学习技术，所述生物实体标记系统将文本中提到的术语与数据库中的条目相关联。生物实体标注是极具挑战性的，因为新奇，同义词和模糊性与文本中代表生物实体的术语相关联。该项目包括构建生物实体词典和利用在线资源获取消歧知识。它还包括字典查找方法的开发和机器学习技术的应用，以解决歧义，发现新奇和识别同义词。该研究将产生若干成果，并将丰富的基因/蛋白质名称、参考文献和其他注释字段的信息整合到UniProt/PIR数据库中，这是一项正在进行的蛋白质数据库国际努力。该项目为进一步加强哥伦比亚大学、乔治敦大学医学中心和巴尔的摩县的马里兰州大学之间的合作提供了机会。该项目还通过让研究生和本科生参与整个项目来整合教育和研究活动。