Turnkey solution for image phenotype classification

图像表型分类的交钥匙解决方案

• 批准号: 8130804
• 负责人: STEVEN J ALTSCHULER
• 金额: $ 26.93万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-19 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8130804
• 关键词: Area; Basic Science; Biological; Biological Assay; Cells; Cellular Morphology; Classification; Communities; Computer software; Computers; Data; Data Set; Development; Fluorescence Microscopy; Goals; Image; Image Analysis; Knowledge; Neurosciences; Phenotype; Process; Proteins; RNA Interference; Research; Research Personnel; Resistance; Running; Signal Transduction; Software Tools; Solutions; Source; Technology; Testing; Therapeutic; Toxic effect; Work; anticancer research; base; drug discovery; response; tool; yeast genetics

DESCRIPTION (provided by applicant): Recent advances in fluorescence microscopy technology have enabled the development of a wide variety of image-based assays that are applicable to such areas as drug discovery, RNAi screens in neuroscience, yeast genetics, and cancer research. These assays are rich in phenotypic information such as cell morphology and protein localization and activity. However, despite the technological progress and promise of rich new sources of cellular data, significant difficulties remain in the interpretation of image data by researchers. Most notably, easy to use tools for producing rapid and comprehensive summaries of image data are lacking. A turnkey software package for the analysis of image data will be developed, tested, and deployed to the research community. The software will automatically classify experimental conditions by image phenotypes, and display specific examples of image phenotypes that distinguish experimental conditions. This classification will be able to group images either with or without prior knowledge of specific cell phenotypes. The software will be capable of running on a single desktop computer, requiring no specialized expertise for most biological applications. Thus, the goal of this proposal is to make the extraction of summarized information from large image data sets as routine as analyzing microarray data. The results of this work will provide a significant expansion of the capacity of fluorescence microscopy as a basic research tool, providing researchers with a new ability to inquire into such fundamental processes as the mechanisms of cellular signaling, and cellular responses to therapeutics such as sensitivity, resistance, and toxicity. Project narrative: In this proposal, a turnkey software tool for the automatic identification of important cellular phenotypes in images obtained by fluorescence microscopy will be developed, tested and delivered to the research community. This is important because general tools for producing rapid and compressive summaries of image data are lacking. The results of this work will provide a significant expansion of the capacity of fluorescence microscopy as a basic research tool, providing researchers with a new ability to inquire into such fundamental processes as the mechanisms of cellular signaling, and cellular responses to therapeutics such as sensitivity, resistance, and toxicity.

描述（由申请人提供）：荧光显微镜技术的最新进展使得能够开发多种基于图像的测定，这些测定适用于药物发现、神经科学中的 RNAi 筛选、酵母遗传学和癌症研究等领域。这些检测富含细胞形态、蛋白质定位和活性等表型信息。然而，尽管技术进步并有望提供丰富的新细胞数据来源，但研究人员在解释图像数据方面仍然存在重大困难。最值得注意的是，缺乏用于生成快速、全面的图像数据摘要的易于使用的工具。 用于图像数据分析的交钥匙软件包将被开发、测试并部署到研究界。软件将自动按图像表型对实验条件进行分类，并显示区分实验条件的图像表型的具体示例。这种分类将能够在事先了解或不了解特定细胞表型的情况下对图像进行分组。该软件将能够在一台台式计算机上运行，​​大多数生物应用不需要专业知识。 因此，该提案的目标是使从大型图像数据集中提取汇总信息成为分析微阵列数据的常规。这项工作的结果将显着扩展荧光显微镜作为基础研究工具的能力，为研究人员提供新的能力来研究细胞信号传导机制以及细胞对治疗的反应（例如敏感性、耐药性和毒性）等基本过程。 项目简介：在本提案中，将开发、测试并交付给研究界一个交钥匙软件工具，用于自动识别荧光显微镜获得的图像中的重要细胞表型。这很重要，因为缺乏用于生成图像数据的快速和压缩摘要的通用工具。这项工作的结果将显着扩展荧光显微镜作为基础研究工具的能力，为研究人员提供新的能力来研究细胞信号传导机制以及细胞对治疗的反应（例如敏感性、耐药性和毒性）等基本过程。