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231225s2020 xx |||||o 00| ||eng c |
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|a 10.21105/joss.02579
|2 doi
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|a eng
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| 100 |
1 |
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|a Hoffer, John
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Minerva
|b a light-weight, narrative image browser for multiplexed tissue images
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|c 2020
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 31.03.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Advances in highly multiplexed tissue imaging are transforming our understanding of human biology by enabling detection and localization of 10-100 proteins at subcellular resolution (Bodenmiller, 2016). Efforts are now underway to create public atlases of multiplexed images of normal and diseased tissues (Rozenblatt-Rosen et al., 2020). Both research and clinical applications of tissue imaging benefit from recording data from complete specimens so that data on cell state and composition can be studied in the context of overall tissue architecture. As a practical matter, specimen size is limited by the dimensions of microscopy slides (2.5 × 7.5 cm or ~2-8 cm2 of tissue depending on shape). With current microscopy technology, specimens of this size can be imaged at sub-micron resolution across ~60 spectral channels and ~106 cells, resulting in image files of terabyte size. However, the rich detail and multiscale properties of these images pose a substantial computational challenge (Rashid et al., 2020). See Rashid et al. (2020) for an comparison of existing visualization tools targeting these multiplexed tissue images
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|a Journal Article
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1 |
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|a Rashid, Rumana
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Muhlich, Jeremy L
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Chen, Yu-An
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Russell, Douglas Peter William
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ruokonen, Juha
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Krueger, Robert
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Pfister, Hanspeter
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Santagata, Sandro
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sorger, Peter K
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t Journal of open source software
|d 2017
|g 5(2020), 54 vom: 04.
|w (DE-627)NLM275088057
|x 2475-9066
|7 nnns
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| 773 |
1 |
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|g volume:5
|g year:2020
|g number:54
|g day:04
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|u http://dx.doi.org/10.21105/joss.02579
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