Automated subcellular location determination and high-throughput microscopy

被引：258

作者：

Glory, Estelle

Murphy, Robert F.

机构：

[1] Carnegie Mellon Univ, Ctr Bioimage Informat, Pittsburgh, PA 15213 USA

[2] Carnegie Mellon Univ, Mol Biosensor & Imaging Ctr, Pittsburgh, PA 15213 USA

[3] Carnegie Mellon Univ, Dept Biol Sci, Pittsburgh, PA 15213 USA

[4] Carnegie Mellon Univ, Dept Biomed Engn & Machine Learning, Pittsburgh, PA 15213 USA

来源：

DEVELOPMENTAL CELL | 2007年 / 12卷 / 01期

关键词：

PROTEIN LOCALIZATION; FLUORESCENCE MICROSCOPY; PATTERNS; CLASSIFICATION; RECOGNITION; CELLS; SEGMENTATION; PROTEOMICS; TRACKING; IMAGES;

D O I：

10.1016/j.devcel.2006.12.007

中图分类号：

Q2 [细胞生物学];

学科分类号：

071013 [干细胞生物学];

摘要：

Dramatic advances in methods for protein tagging and the development of fully automated microscopes enable collection of unprecedented volumes of image data on the subcellular location of proteins in live cells. Combining these approaches with machine learning methods promises to provide systematic, high-resolution pattern information on a proteome-wide basis. © 2007 Elsevier Inc. All rights reserved.

引用

收藏

页码：7 / 16

页数：10

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