ADAPTIVE PERSONALIZED INFORMATION MANAGEMENT FOR BIOLOGISTS

生物学家的自适应个性化信息管理

• 批准号: 8075593
• 负责人: WILLIAM W COHEN
• 金额: $ 27.44万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-11 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8075593
• 关键词: Address; Biological; Biological Phenomena; Biology; Collection; Communities; Computers; Data Sources; Databases; Development; Eukaryota; Genes; Goals; Grant; Individual; Information Management; Information Resources; Knowledge; Learning; Literature; Machine Learning; Medical; Metric; Monitor; Output; Persons; Process; Proteins; Publications; Research; Research Personnel; Ribosomes; Role; Scheme; Scientist; Solutions; Source; Staging; Structure; Surface; System; Techniques; Technology; Text; Time; Training; Work; base; design; experience; knowledge base; man; novel strategies; programs; tool

DESCRIPTION (provided by applicant): We propose development of an adaptive, personalizable, information management tool, which can be configured and trained by an individual biologist to most effectively exploit the particular knowledge bases and document collections that are most useful for him or her. The proposed tool represents a novel approach for monitoring scientific progress in biology, which has become a formidable task. We will exploit recent advances in machine learning and database systems to develop a useful approximation to a personalized biological knowledge base f.i.i.e., single information resource that would include all the knowledge sources on which a biologist relies. More specifically, we propose a scheme for loosely integrating both structured information and unstructured text, and then querying the integrated information using easily-formulated similarity queries. The system will also learn from every episode in which a biologist seeks information. The research team on this project includes a computer scientist and two biologists. The proposed work will make systems for monitoring scientific progress in biology more effective. This will make biologists, clinicians and medical researchers better able to track advances in the biomedical literature that are relevant to their work.

描述(由申请人提供)：我们建议开发一种自适应的、可个性化的信息管理工具，该工具可以由个别生物学家进行配置和培训，以最有效地利用对他或她最有用的特定知识库和文档集合。拟议的工具代表了一种监测生物学科学进步的新方法，这已成为一项艰巨的任务。我们将利用机器学习和数据库系统中的最新进展来开发一种有用的近似于个性化生物知识库的方法，即，单个信息源将包括生物学家所依赖的所有知识源。更具体地说，我们提出了一种将结构化信息和非结构化文本松散地集成在一起，然后使用易于表达的相似性查询来查询集成信息的方案。该系统还将从生物学家寻找信息的每一集中学习。该项目的研究团队包括一名计算机科学家和两名生物学家。拟议的工作将使监测生物学科学进步的系统更加有效。这将使生物学家、临床医生和医学研究人员能够更好地跟踪与他们的工作相关的生物医学文献的进展。

项目成果

Information Extraction as Link Prediction: Using Curated Citation Networks to Improve Gene Detection.

信息提取作为链接预测：使用策划的引文网络来改进基因检测。

• DOI: 10.1007/978-3-642-03417-6_53
• 发表时间: 2009
• 期刊: WASA ... : International Conference on Wireless Algorithms, Systems, and Applications : proceedings. WASA
• 作者: Arnold,Andrew;Cohen,WilliamW
• 通讯作者: Cohen,WilliamW