The genomic sequencing revolution has led to rapid growth in sequencing of genes and proteins, and attention is now turning to the function of the encoded proteins. In this respect, microscope imaging of a protein's subcellular location is proving invaluable. High-throughput methods mean that it is now possible to capture images of hundreds of protein localisations quickly and relatively inexpensively, and hence genome-wide protein localisation studies are becoming feasible. However, to a large degree the analysis and localisation classification are still performed by the slow, coarse-grained and possibly biased process of manual inspection. As a step towards dealing with the fast growth in subcellular image data the Automated Sub-cellular Classification system (ASPiC) has been developed: a pipeline for taking cell images, generating statistics and classifying using SVMs. Here, the pipeline is described and correct classification rates of 93.5% and 86.5% on two 8-class subcellular localisationdatasets are reported. In addition we present a survey of other important applications of cell image statistics. The complete image sets are being made available with the aim of encouraging further research into automated cell image analysis and classification.
基因组测序革命导致基因和蛋白质测序的快速增长,现在人们的注意力转向了编码蛋白质的功能。在这方面,证明蛋白质亚细胞位置的显微镜成像非常重要。高通量方法意味着现在可以快速且相对便宜地捕获数百个蛋白质定位的图像,因此全基因组蛋白质定位研究正变得可行。但是,在很大程度上,分析和定位分类仍然是由缓慢,粗粒度且可能有偏差的手动检查过程执行的。作为应对亚细胞图像数据快速增长的一步,已开发了自动化亚细胞分类系统(ASPiC):用于获取细胞图像,生成统计数据并使用SVM进行分类的管道。在这里,描述了管道,并报告了在两个8类亚细胞定位数据集上正确分类率为93.5%和86.5%。此外,我们还介绍了细胞图像统计其他重要应用的概况。目前正在提供完整的图像集,以鼓励对自动细胞图像分析和分类进行进一步的研究。 P>
The University of Queensland, Brisbane, Qld, Australia;
机译:“ MTB细胞计数器”是一种多功能工具,用于半自动定量荧光显微镜图像中的亚细胞表型。质体,核和过氧化物酶体的案例研究
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机译:自动亚细胞表型分类
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机译:一种两步登记分类方法以实现高通量温室植物表型多峰图像的自动分割
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