III-CXT-Large: Collaborative Research: Interactive and intelligent searching of biological images by query and network navigation with learning capabilities

III-CXT-Large：协作研究：通过具有学习能力的查询和网络导航对生物图像进行交互式和智能搜索

• 批准号: 0808770
• 负责人: Eamonn Keogh
• 金额: $ 110.91万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0808770&HistoricalAwards=false
• 关键词: III; CXT; Large; Collaborative; Research

A fundamental and hard question in biology is identification of organisms. This proposal focuses on identification of nematodes, which are particularly difficult to identify, with the average identification requiring significant time and high level of expertise. Nematodes have direct and significant effect on humans, other animals, and agriculture. Four species of nematode parasites infect over 2 billion people worldwide, and one type of nematode causes one-third of the total estimated worldwide annual yield losses to all soybean pathogens. The current limiting factors for identification are the lack of tools and automation, the need for image comparison off-line and a need for significant expertise. To enable seasoned researchers as well as students to use resources, the team will build on image searching work, using a set of images that will make nematode identification a simple process of point and click. In addition to enabling research by harnessing data and experience of experts, the work may make biology more accessible. The team will build a computer-assisted interactive navigator that will intelligently assist and learn from the user. The work can be extended to many other biological data sets. The research challenges include extraction of features and similarity functions, and the mining, clustering, and anomaly detection for image and non-image data. Graduate students are engaged in the research and outreach involving high school students is also planned.

生物学中的一个基本而又困难的问题是生物体的识别。 该提案侧重于线虫的识别，线虫特别难以识别，平均识别需要大量时间和高水平的专业知识。 线虫对人类、其他动物和农业有着直接和重大的影响。 四种线虫寄生虫感染全球超过20亿人，其中一种线虫导致所有大豆病原体造成的全球估计年产量损失总额的三分之一。 目前识别的限制因素是缺乏工具和自动化，需要离线图像比较以及需要大量的专业知识。 为了使经验丰富的研究人员和学生能够使用资源，该团队将建立在图像搜索工作的基础上，使用一组图像，使线虫识别成为一个简单的点击过程。 除了通过利用专家的数据和经验进行研究外，这项工作还可以使生物学更容易获得。 该团队将建立一个计算机辅助的交互式导航器，将智能地帮助和学习用户。 这项工作可以扩展到许多其他生物数据集。 研究的挑战包括特征和相似性函数的提取，以及图像和非图像数据的挖掘，聚类和异常检测。 研究生正在参与研究，还计划开展涉及高中生的外联活动。