Information Technology Research (ITR): Next-Generation Bio-Molecular Imaging and Information Discovery

信息技术研究 (ITR)：下一代生物分子成像和信息发现

• 批准号: 0331697
• 负责人: Bangalore Manjunath
• 金额: --
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-10-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0331697&HistoricalAwards=false
• 关键词: Information; Technology; Research; ITR; Next

This collaborative project brings together a strong multi-institutional interdisciplinary team of investigators to study and advance the current understanding of cellular and sub-cellular events. Continuing technological advances in fluorescence and atomic-force microscopy allow scientists to observe molecular function, distribution, and interrelationships in living cells. However, a full understanding of tens of thousands of proteins and the complex molecular processes they engage in requires a voluminous amount of image data, which currently must be analyzed by visual inspection. To facilitate such an analysis, researchers from the four participating institutions are focusing on three main research thrusts. First, next-generation intelligent imaging involves information processing at the sensor level to enable high-speed and super-resolution imaging. The goal is to enable biologists to study cellular processes at resolutions in time and space that are not possible with current technologies. The second research thrust is pattern recognition and data mining as applied to bio-molecular image collections. Salient features that characterize the underlying patterns in cells and tissues need to be computed for the vast volumes of images acquired through automated microscopy. Third, a distributed database of bio-molecular images is being created. The merging of pattern-recognition and data-mining tools with new, powerful methods for indexing, data modeling, and collaboration, is aimed at creating a unique infrastructure that greatly facilitates image bioinformatics, thus complementing recent revolutionary advances in genomics. The outcome of this research will lead to new and novel information-processing methods for bio-molecular image data. Efficient and effective representation of such data will enable researchers to search and browse through large collections of image and video data and look for similar patterns in such datasets, thus facilitating information discovery. During its five-year duration, this project will develop, test, and deploy a distributed database of bio-molecular image data accessible to researchers around the world. The impact of the distributed database will be through large-scale biology in which the results of a single experiment can be globally correlated with the results from other groups of scientists, thus accelerating discovery of dynamic relationships between structure and function in complex biological systems.The project will develop new courses, and will facilitate student exchanges, semi-annual meetings, and workshops, benefiting students at all levels. This project will train a new generation of biologists, computer scientists and engineers well versed in the imaging and information-processing sciences at the forefront of next-generation biotechnology. Partnership will be established with institutions with large populations of students from groups underrepresented in science and engineering, such as the California State Universities at Fresno and San Bernardino and the Universidad Metropolitan in Puerto Rico, for undergraduate recruitment and outreach. An effective mode of outreach for students is to educate their teachers, and the project will offer summer fellowships for elementary, high-school, college, and university teachers.

这个协作项目汇集了一个强大的多机构跨学科研究人员团队，以研究和促进当前对细胞和亚细胞事件的理解。荧光和原子力显微镜的持续技术进步使科学家可以观察到活细胞中的分子功能，分布和相互关系。但是，对成千上万蛋白的充分理解及其参与的复杂分子过程需要大量的图像数据，目前必须通过视觉检查对其进行分析。为了促进这样的分析，来自四个参与机构的研究人员正在关注三个主要研究作用。首先，下一代智能成像涉及在传感器级别的信息处理，以实现高速和超分辨率成像。目的是使生物学家能够研究当前技术不可能的时间和空间分辨率的细胞过程。第二个研究推力是应用于生物分子图像收集的模式识别和数据挖掘。需要计算通过自动显微镜获得的大量图像来计算细胞和组织中基础模式的显着特征。第三，正在创建生物分子图像的分布式数据库。模式识别和数据挖掘工具与新的，有力的索引，数据建模和协作的方法的合并旨在创建一个独特的基础架构，从而极大地促进了图像生物信息学的促进，从而补充了最近的革命性基因组学的进步。这项研究的结果将导致生物分子图像数据的新的和新颖的信息处理方法。此类数据的有效表示形式将使研究人员能够通过大量图像和视频数据搜索和浏览，并在此类数据集中寻找相似的模式，从而促进信息发现。 在五年的持续时间内，该项目将开发，测试和部署世界各地研究人员可以访问的生物分子图像数据的分布式数据库。分布式数据库的影响将是通过大型生物学的影响，在大型生物学中，单个实验的结果可以与其他科学家组的结果在全球范围内相关，从而加速了复杂生物系统中结构和功能之间动态关系的发现。该项目将培训新一代的生物学家，计算机科学家和工程师精通下一代生物技术最前沿的成像和信息处理科学。 合作伙伴关系将与机构建立，来自科学和工程领域不足的团体的大量学生，例如弗雷斯诺和圣贝纳迪诺的加利福尼亚州立大学，以及波多黎各的大都会大学，用于招聘和外展。对学生的有效宣传方式是教育他们的老师，该项目将为小学，高中，大学和大学老师提供夏季奖学金。