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Understanding Figures & Captions for Location Proteomics

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基本信息

批准号：
7033080
负责人：
WILLIAM W COHEN
金额：
$ 13.35万
依托单位：
CARNEGIE-MELLON UNIVERSITY
依托单位国家：
美国
项目类别：
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/7033080
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

项目摘要

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）：该提案旨在对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、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。赞助商将提供生物医学研究文化和实践的指导。该培训机构拥有跨学科研究的悠久传统和尖端蛋白质组学方法的具体专业知识。候选人将完全致力于培训和研究的结合。该研究计划是基于组织和总结大量生物医学文献中的知识的迫切需要。精心策划的数据库的创建和维护成本高昂；不估计断言的置信度；并且不允许意见分歧。信息提取（IE）方法可以通过从生物医学文本中自动提取某些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（例如图像和图表）来说明的。我们提出的研究的广泛主题是，通过从图形图像及其随附的标题中联合提取信息，可以更好地访问在线科学出版物中的信息。除了 Murphy 小组之前的某些工作之外，之前的生物医学 IE 系统并未尝试从图像数据中提取信息，而只是从文本中提取信息。该提案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主要焦点是一致的：表达基因的鉴定和它们编码的蛋白质的描述。受近期大型项目的推动，当前生物医学研究的主要焦点是表达基因的识别和它们编码的蛋白质的描述（或注释），墨菲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已应用于从在线生物医学期刊文章中获取的图像。该系统将被扩展以创建一个强大、全面的工具集，用于从在线期刊中的文本和图像中提取、验证和查询生物相关信息。基于该工具包，将开发一套工具来帮助研究人员识别和定位在线期刊中的信息。完成拟议的培训后，候选人将能够在机器学习应用到生物医学研究中使用的一系列实验方法方面占据领导地位。