Current challenges in bioimage database design

Ambuj K. Singh, Arnab Bhattacharya, Vebjorn Ljosa
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引用次数: 1

Abstract

Information technology research has played a significant role in the high-throughput acquisition and analysis of biological information. The tremendous amount of information gathered from genomics in the past decade is being complemented by knowledge from comprehensive, systematic studies of the properties and behaviors of all proteins and other biomolecules. Understanding complex systems such as the nervous system requires the high-resolution imaging of molecules and cells and the analysis of these images in order to understand how distribution patterns (e.g., the localization of specific neuron types within a region of the central nervous system, or the localization of molecules at the subcellular level) change in response to stress, injury, aging, and disease. We discuss two kinds of bioimage data: retinal images and microtubule images. We argue that supporting effective access to them requires new database techniques for description of probabilistic and interpreted data, and analysis of spatial and temporal information. The developed techniques are being implemented in a publicly available bioimage database.
生物图像数据库设计面临的挑战
信息技术研究在生物信息的高通量采集和分析中发挥了重要作用。在过去的十年中,从基因组学中收集到的大量信息正在被对所有蛋白质和其他生物分子的性质和行为的全面、系统的研究所补充。理解复杂的系统,如神经系统,需要分子和细胞的高分辨率成像,并对这些图像进行分析,以了解分布模式(例如,中枢神经系统区域内特定神经元类型的定位,或亚细胞水平上分子的定位)如何响应压力、损伤、衰老和疾病而变化。我们讨论了两种生物图像数据:视网膜图像和微管图像。我们认为,支持对它们的有效访问需要新的数据库技术来描述概率和解释数据,以及分析空间和时间信息。开发的技术正在一个公开的生物图像数据库中实施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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