期刊
CELL SYSTEMS
卷 6, 期 6, 页码 636-653出版社
CELL PRESS
DOI: 10.1016/j.cels.2018.06.001
关键词
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资金
- Finnish TEKES FiDiPro Fellow Grant [40294/13]
- European Association for Cancer Research (EACR) [573]
- NEUBIAS COST Action (European Cooperation in Science and Technology) [CA15124]
- European Regional Development Funds [GINOP-2.3.2-15-2016-00006, GINOP-2.3.2-152016-00026]
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.
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