SGER: Detection of Subtle Spatio-Temporal Patterns in Biological Imaging Using Permutation Methods

SGER：使用排列方法检测生物成像中微妙的时空模式

• 批准号: 9727699
• 负责人: Raul Valdes-Perez
• 金额: $ 10万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-15 至 1999-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9727699&HistoricalAwards=false
• 关键词: SGER; Detection; Subtle; Spatio; Temporal

Computers have become indispensable in much of current biology, in their roles of acquiring, processing, visualizing, and storing complex four- dimensional (space & time) data. However, the final stage in biological research is going from acquired, processed, and stored data to new biological knowledge. Computerized visualization is used in this stage, but there has been less development of computerized reasoning tools that can complement the biologist's toolkit. This project is developing discovery tools - in the form of novel algorithms and software - to deal with the complex 4D data of biological imaging. These tools will help detect subtle spatio-temporal patterns (i.e., non-randomness) that would be difficult to notice with standard means of analysis. The main technique is based on permutation tests, which have been applied widely in other sciences but not in biological imaging. The challenge is to design algorithms to handle the specific problems posed by typical imaging data.

计算机在当今生物学的许多领域已经成为不可或缺的，它们在获取、处理、可视化和存储复杂的四维（空间和时间）数据方面发挥着作用。然而，生物学研究的最后阶段是从获取、处理和存储数据到新的生物学知识。计算机可视化在这一阶段被使用，但计算机推理工具的发展很少，可以补充生物学家的工具包。这个项目正在开发发现工具——以新颖的算法和软件的形式——来处理生物成像的复杂的四维数据。这些工具将有助于检测微妙的时空模式（即非随机性），这些模式很难用标准的分析方法发现。主要的技术是基于排列测试，这在其他科学领域得到了广泛的应用，但在生物成像领域还没有得到应用。挑战在于设计算法来处理典型成像数据所带来的具体问题。