Understanding Figures & Captions for Location Proteomics

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• 批准号: 6709988
• 负责人: WILLIAM W COHEN
• 金额: $ 16.17万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-01 至 2007-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6709988
• 关键词: bioengineering /biomedical engineering; bioinformatics; computational biology; computer program /software; computer system design /evaluation; fluorescence microscopy; gene expression; image processing; online computer; protein localization; proteomics; publications; training

DESCRIPTION (provided by applicant): This proposal is for mentored training in the molecular biosciences of an established computer scientist. The training plan includes basic and advanced course work in modern biology, interactions with biological research groups, attendance at seminars and conferences, and laboratory training. Mentoring on the culture and practices of biomedical research will be provided by the sponsor. The training institution has a longstanding tradition of interdisciplinary research and specific expertise in cutting edge proteomics methods. The candidate will be fully committed to a combination of training and research. The research plan is based on the critical need to organize and summarize the knowledge in the vast biomedical literature. Curated databases are expensive to create and maintain; do not estimate confidence of assertions; and do not allow for divergence of opinions. Information extraction (IE) methods can be used to partially overcome these limitations by automatically extracting certain types of information from biomedical text. In most genres of scientific publication, the most important results in a paper are illustrated in non-textual forms, such as images and graphs. The broad thesis underlying our proposed research is that one can provide better access to the information in online scientific publications by extracting information jointly from figure images and their accompanying captions. With the exception of certain previous work by the Murphy group, previous biomedical IE systems have not attempted to extract information from image data, only text. This proposal addresses these issues in the specific context of fluorescence microscope images depicting the subcellular localization of proteins. This goal is consonant with a major focus of current biomedical research: the identification of expressed genes and the description of the proteins they encode. Motivated by recent large-scale projects which major focus of current biomedical research is the identification of expressed genes and the description (or annotation) of the proteins they encode, the Murphy group has developed automated systems for recognizing subcellular structures in 2D and 3D images. Automated image analysis techniques have also been applied to images harvested from online biomedical journal articles. This system will be extended to create a robust, comprehensive toolset for extracting, verifying and querying biologically relevant information from the text and images found in online journals. Based on this toolkit, a set of tools will be developed for aiding researchers to identify and locate information found in online journals. Upon completion of the proposed training, the candidate will be well placed to take a leadership position in machine learning applications to the range of experimental methods used in biomedical research.

描述(由申请人提供)： 这项建议是为了对一位知名计算机科学家进行分子生物科学方面的指导培训。培训计划包括现代生物学的基础和高级课程工作，与生物研究小组的互动，参加研讨会和会议，以及实验室培训。赞助商将提供有关生物医学研究的文化和实践的指导。该培训机构在跨学科研究方面有着悠久的传统，在尖端蛋白质组学方法方面拥有专门的专业知识。应聘者将完全致力于培训和研究的结合。研究计划的基础是组织和总结大量生物医学文献中的知识的迫切需要。精心策划的数据库创建和维护成本高昂；不评估断言的可信度；并且不允许意见分歧。信息提取(IE)方法可以通过自动从生物医学文本中提取特定类型的信息来部分克服这些限制。 在大多数类型的科学出版物中，论文中最重要的结果都是以非文本形式说明的，如图像和图表。我们提出的研究的基本论点是，通过联合从图形图像及其附带的说明中提取信息，可以更好地获取在线科学出版物中的信息。除了墨菲小组之前的某些工作之外，以前的生物医学IE系统没有尝试从图像数据中提取信息，只尝试从文本中提取信息。 这项建议在描述蛋白质亚细胞定位的荧光显微镜图像的特定背景下解决了这些问题。这一目标与当前生物医学研究的一个主要焦点是一致的：识别表达的基因并描述它们编码的蛋白质。在最近的大型项目的推动下，目前生物医学研究的主要焦点是识别表达的基因及其编码的蛋白质的描述(或注释)，Murphy小组开发了用于识别2D和3D图像中的亚细胞结构的自动化系统。自动图像分析技术也被应用于从在线生物医学期刊文章中获取的图像。将扩展该系统，以创建一个强大的综合工具包，用于从在线期刊中找到的文本和图像中提取、核实和查询与生物有关的信息。在这个工具包的基础上，将开发一套工具，帮助研究人员识别和定位在线期刊中的信息。在完成拟议的培训后，候选人将处于有利地位，在生物医学研究中使用的各种实验方法的机器学习应用方面处于领先地位。