Chapter 17: bioimage informatics for systems pharmacology.

Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA inte...

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Autores principales:	Fuhai Li, Zheng Yin, Guangxu Jin, Hong Zhao, Stephen T C Wong
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/01ae13ab014e4a71b79737b02f84b133
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spelling	oai:doaj.org-article:01ae13ab014e4a71b79737b02f84b1332021-11-18T05:52:12ZChapter 17: bioimage informatics for systems pharmacology.1553-734X1553-735810.1371/journal.pcbi.1003043https://doaj.org/article/01ae13ab014e4a71b79737b02f84b1332013-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23633943/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.Fuhai LiZheng YinGuangxu JinHong ZhaoStephen T C WongPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 4, p e1003043 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Fuhai Li Zheng Yin Guangxu Jin Hong Zhao Stephen T C Wong Chapter 17: bioimage informatics for systems pharmacology.
description	Recent advances in automated high-resolution fluorescence microscopy and robotic handling have made the systematic and cost effective study of diverse morphological changes within a large population of cells possible under a variety of perturbations, e.g., drugs, compounds, metal catalysts, RNA interference (RNAi). Cell population-based studies deviate from conventional microscopy studies on a few cells, and could provide stronger statistical power for drawing experimental observations and conclusions. However, it is challenging to manually extract and quantify phenotypic changes from the large amounts of complex image data generated. Thus, bioimage informatics approaches are needed to rapidly and objectively quantify and analyze the image data. This paper provides an overview of the bioimage informatics challenges and approaches in image-based studies for drug and target discovery. The concepts and capabilities of image-based screening are first illustrated by a few practical examples investigating different kinds of phenotypic changes caEditorsused by drugs, compounds, or RNAi. The bioimage analysis approaches, including object detection, segmentation, and tracking, are then described. Subsequently, the quantitative features, phenotype identification, and multidimensional profile analysis for profiling the effects of drugs and targets are summarized. Moreover, a number of publicly available software packages for bioimage informatics are listed for further reference. It is expected that this review will help readers, including those without bioimage informatics expertise, understand the capabilities, approaches, and tools of bioimage informatics and apply them to advance their own studies.
format	article
author	Fuhai Li Zheng Yin Guangxu Jin Hong Zhao Stephen T C Wong
author_facet	Fuhai Li Zheng Yin Guangxu Jin Hong Zhao Stephen T C Wong
author_sort	Fuhai Li
title	Chapter 17: bioimage informatics for systems pharmacology.
title_short	Chapter 17: bioimage informatics for systems pharmacology.
title_full	Chapter 17: bioimage informatics for systems pharmacology.
title_fullStr	Chapter 17: bioimage informatics for systems pharmacology.
title_full_unstemmed	Chapter 17: bioimage informatics for systems pharmacology.
title_sort	chapter 17: bioimage informatics for systems pharmacology.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/01ae13ab014e4a71b79737b02f84b133
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Chapter 17: bioimage informatics for systems pharmacology.

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