Binary pattern dictionary learning for gene expression representation in drosophila imaginal discs
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00304934" target="_blank" >RIV/68407700:21230/17:00304934 - isvavai.cz</a>
Result on the web
<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/borovec/Borovec-MCBMIIA2016.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/borovec/Borovec-MCBMIIA2016.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-54427-4_40" target="_blank" >10.1007/978-3-319-54427-4_40</a>
Alternative languages
Result language
angličtina
Original language name
Binary pattern dictionary learning for gene expression representation in drosophila imaginal discs
Original language description
We present an image processing pipeline which accepts a largenumber of images, containing spatial expression information for thousands of genes in Drosophila imaginal discs. We assume that the geneactivations are binary and can be expressed as a union of a small setof non-overlapping spatial patterns, yielding a compact representationof the spatial activation of each gene. This lends itself well to furtherautomatic analysis, with the hope of discovering new biological relationships. Traditionally, the images were labeled manually, which was verytime consuming. The key part of our work is a binary pattern dictionarylearning algorithm, that takes a set of binary images and determinesa set of patterns, which can be used to represent the input images witha small error. We also describe the preprocessing phase, where input images are segmented to recover the activation images and spatially alignedto a common reference. We compare binary pattern dictionary learningto existing alternative methods on synthetic data and also show resultsof the algorithm on real microscopy images of the Drosophila imaginaldiscs.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA14-21421S" target="_blank" >GA14-21421S: Automatic analysis of spatial gene expression patterns</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision – ACCV 2016 Workshops
ISBN
978-3-319-54426-7
ISSN
0302-9743
e-ISSN
—
Number of pages
15
Pages from-to
555-569
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Taipei, Taiwan
Event date
Nov 20, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000426193700040