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Symposium on geometry processing : [proceedings]. Symposium on Geometry Processing最新文献

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A geometric database for gene expression data 基因表达数据的几何数据库
Pub Date : 2003-06-23 DOI: 10.2312/SGP/SGP03/166-176
J. Warren, T. Ju, G. Eichele, C. Thaller, W. Chiu, J. Carson
As the logical next step after sequencing the mouse genome, biologists have developed laboratory methods for rapidly determining where each of the 30K genes in the mouse genome is synthesizing protein. Applying these methods to the mouse brain, biologists are currently generating large numbers of 2D cross-sectional images that record the expression pattern for each gene in the mouse genome. In this paper, we describe the structure of a geometric database for the mouse brain that allows biologists to organize and search this gene expression data. The central component of this database is an atlas that explicitly partitions the mouse brain into key anatomical regions. This atlas is represented as a Catmull-Clark subdivision mesh with anatomical regions separated by a network of B-spline crease curves. New gene expression images are added to the database by deforming this atlas onto each image using techniques developed for fitting subdivision surfaces to scatter data. Due to this partitioning of the subdivision mesh, user queries comparing expression data between various genes can be restricted to anatomical regions without difficulty while the multi-resolution structure of the subdivision mesh allows these queries to be processed efficiently.
作为小鼠基因组测序后合乎逻辑的下一步,生物学家已经开发出实验室方法来快速确定小鼠基因组中30K个基因中的每个基因在何处合成蛋白质。将这些方法应用于小鼠大脑,生物学家目前正在生成大量的二维横断面图像,记录小鼠基因组中每个基因的表达模式。在本文中,我们描述了一个老鼠大脑的几何数据库的结构,允许生物学家组织和搜索这些基因表达数据。该数据库的核心组成部分是一个明确地将小鼠大脑划分为关键解剖区域的图谱。该图谱表示为Catmull-Clark细分网格,其解剖区域由b样条折痕曲线网络分隔。新的基因表达图像被添加到数据库中,通过使用拟合细分表面来分散数据的技术将图谱变形到每个图像上。由于这种细分网格的划分,用户查询比较不同基因之间的表达数据可以很容易地限制在解剖区域,而细分网格的多分辨率结构允许这些查询被有效地处理。
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引用次数: 53
A geometric database for gene expression data. 基因表达数据的几何数据库。
Tao Ju, Joe Warren, Gregor Eichele, Christina Thaller, Wah Chiu, James Carson

As the logical next step after sequencing the mouse genome, biologists have developed laboratory methods for rapidly determining where each of the 30K genes in the mouse genome is synthesizing protein. Applying these methods to the mouse brain, biologists are currently generating large numbers of 2D cross-sectional images that record the expression pattern for each gene in the mouse genome. In this paper, we describe the structure of a geometric database for the mouse brain that allows biologists to organize and search this gene expression data. The central component of this database is an atlas that explicitly partitions the mouse brain into key anatomical regions. This atlas is represented as a Catmull-Clark subdivision mesh with anatomical regions separated by a network of B-spline crease curves. New gene expression images are added to the database by deforming this atlas onto each image using techniques developed for fitting subdivision surfaces to scatter data. Due to this partitioning of the subdivision mesh, user queries comparing expression data between various genes can be restricted to anatomical regions without difficulty while the multi-resolution structure of the subdivision mesh allows these queries to be processed efficiently.

作为小鼠基因组测序后合乎逻辑的下一步,生物学家已经开发出实验室方法来快速确定小鼠基因组中30K个基因中的每个基因在何处合成蛋白质。将这些方法应用于小鼠大脑,生物学家目前正在生成大量的二维横断面图像,记录小鼠基因组中每个基因的表达模式。在本文中,我们描述了一个老鼠大脑的几何数据库的结构,允许生物学家组织和搜索这些基因表达数据。该数据库的核心组成部分是一个明确地将小鼠大脑划分为关键解剖区域的图谱。该图谱表示为Catmull-Clark细分网格,其解剖区域由b样条折痕曲线网络分隔。新的基因表达图像被添加到数据库中,通过使用拟合细分表面来分散数据的技术将图谱变形到每个图像上。由于这种细分网格的划分,用户查询比较不同基因之间的表达数据可以很容易地限制在解剖区域,而细分网格的多分辨率结构允许这些查询被有效地处理。
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引用次数: 0
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Symposium on geometry processing : [proceedings]. Symposium on Geometry Processing
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