Probabilistic Modeling of Information from Images and Text in Online Journals

在线期刊中图像和文本信息的概率建模

• 批准号: 7458122
• 负责人: Robert F Murphy
• 金额: $ 25.6万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7458122
• 关键词: 3-Dimensional; Architecture; Area; Arts; Biomedical Research; Blast Cell; Classification; Collection; Computer software; Condition; Data; Data Set; Databases; Depth; Exhibits; Facility Construction Funding Category; Feeds; Fluorescence Microscopy; Future; Genes; Glycine decarboxylase; Goals; Harvest; Image; Individual; Journals; Literature; Location; Measures; Methods; Names; Numbers; Ontology; Operative Surgical Procedures; Paper; Pattern; Property; Protein Databases; Proteins; Proteomics; PubMed; Publications; Range; Reading; Research Personnel; Resources; Sampling; Scientist; Semantics; Standards of Weights and Measures; Statistical Models; Structure; Subcellular structure; System; Text; Time; Work; abstracting; base; cell type; data mining; fluorescence microscope; genome sequencing; high throughput screening; image processing; improved; intracellular protein transport; open source; programs; protein localization location; research study; text searching; tool; two-dimensional

DESCRIPTION (provided by applicant): The goal of this project is to build a software toolkit that will enable a biologist to create, from a collection of on-line articles, a database of protein subcellular localization information that can be queried, browsed, or used to support data-mining activities. We have developed a system, called SLIF, which can harvest fluorescence microscope images from online papers, analyze them using image-processing methods, and annotate them with information appearing in the accompanying textual description. We propose to improve and extend this system so as to produce a robust, comprehensive toolkit for extracting information about subcellular localization from the text and images found in online journals, as well as analyzing, verifying and querying the resulting body of information.

描述（由申请人提供）：本项目的目标是建立一个软件工具包，使生物学家能够从在线文章的集合中创建一个蛋白质亚细胞定位信息的数据库，该数据库可以查询，浏览或用于支持数据挖掘活动。我们开发了一个名为SLIF的系统，它可以从在线论文中获取荧光显微镜图像，使用图像处理方法对其进行分析，并使用随附的文本描述中出现的信息对其进行注释。我们建议改进和扩展该系统，以便产生一个强大的，全面的工具包，用于从在线期刊中找到的文本和图像中提取有关亚细胞定位的信息，以及分析，验证和查询所产生的信息体。

项目成果

Structured Correspondence Topic Models for Mining Captioned Figures in Biological Literature.

• DOI: 10.1145/1557019.1557031
• 发表时间: 2009
• 期刊: KDD : proceedings. International Conference on Knowledge Discovery & Data Mining
• 作者: Ahmed A;Xing EP;Cohen WW;Murphy RF
• 通讯作者: Murphy RF

A Stacked Graphical Model for Associating Sub-Images with Sub-Captions

用于将子图像与子标题相关联的堆叠图形模型

• DOI: 10.1142/9789812772435_0025
• 发表时间: 2006
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Zhenzhen Kou;William W. Cohen;R. Murphy
• 通讯作者: R. Murphy