AUTOMATED METHODS TO IDENTIFY PATTERNS OF FEATURES BETWEEN CELL TYPES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 关键词： 海量数据分析、可视化和分布协作 摘要： 培养在96孔板中的细胞使用多种方式进行成像 识别超过108个单独的生理细胞测量或 特征，包括核密度，细胞周长， 翻译后修饰，计算 S期细胞百分比与有丝分裂[Gasparri，et al.2004年。J Biol Screen 9：232-243]，细胞质-核易位， 细胞毒性和受体内化的特征。这个 该项目的目标是开发方法来识别 细胞类型之间的特征(单个生理细胞测量)。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Keywords: Volume data analysis, visualization and distribution collaboration Abstract: Cells, grown in a 96 well plate, are imaged using multiple modalities to identify over 108 individual physiological cellular measurements or features, including the nuclear density, cell perimeter, post-translational modifications, specific assays to calculate the percentage of cells in S-phase and mitosis [Gasparri, et al. 2004. J Biomol Screen 9:232-243], cytoplasm-nuclear translocation, characterization of cellular toxicity, and receptor internalization. The goal of this project is to develop approaches to identify patterns of features (individual physiological cellular measurements) between cell types.