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基于细胞分析的大图像数据探索和理解的表型图像分析软件工具。

Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays.

机构信息

KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, 10044 Stockholm, Sweden; Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden.

Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy.

出版信息

Cell Syst. 2018 Jun 27;6(6):636-653. doi: 10.1016/j.cels.2018.06.001.

Abstract

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

摘要

表型图像分析是指使用显微镜图像数据识别细胞特性变化的任务。这些变化是通过基因与环境之间复杂的相互作用产生的,可能是揭示重要生物学现象或了解对候选药物的反应的关键。如今,表型分析很少完全手动进行。现代高通量显微镜产生的高维图像数据大量增加,这需要计算解决方案。在过去的十年中,已经开发了许多软件工具来满足这一需求。它们使用统计学习方法来推断细胞表型与图像数据之间的关系。在这篇综述中,我们检查了非商业表型图像分析软件的优缺点,介绍了该领域的最新进展,确定了挑战,并对未来的可能性进行了展望。

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