Danial, John S. H., Shalaby, Raed, Cosentino, Katia, Mahmoud, Marwa M., Medhat, Fady, Klenerman, David and Saez, Ana J. Garcia (2021). DeepSinse: deep learning-based detection of single molecules. Bioinformatics, 37 (21). S. 3998 - 4001. OXFORD: OXFORD UNIV PRESS. ISSN 1460-2059
Full text not available from this repository.Abstract
Motivation: Imaging single molecules has emerged as a powerful characterization tool in the biological sciences. The detection of these under various noise conditions requires the use of algorithms that are dependent on the end-user inputting several parameters, the choice of which can be challenging and subjective. Results: In this work, we propose DeepSinse, an easily trainable and useable deep neural network that can detect single molecules with little human input and across a wide range of signal-to-noise ratios. We validate the neural network on the detection of single bursts in simulated and experimental data and compare its performance with the best-in-class, domain-specific algorithms.
Item Type: | Journal Article | ||||||||||||||||||||||||||||||||
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URN: | urn:nbn:de:hbz:38-584473 | ||||||||||||||||||||||||||||||||
DOI: | 10.1093/bioinformatics/btab352 | ||||||||||||||||||||||||||||||||
Journal or Publication Title: | Bioinformatics | ||||||||||||||||||||||||||||||||
Volume: | 37 | ||||||||||||||||||||||||||||||||
Number: | 21 | ||||||||||||||||||||||||||||||||
Page Range: | S. 3998 - 4001 | ||||||||||||||||||||||||||||||||
Date: | 2021 | ||||||||||||||||||||||||||||||||
Publisher: | OXFORD UNIV PRESS | ||||||||||||||||||||||||||||||||
Place of Publication: | OXFORD | ||||||||||||||||||||||||||||||||
ISSN: | 1460-2059 | ||||||||||||||||||||||||||||||||
Language: | English | ||||||||||||||||||||||||||||||||
Faculty: | Unspecified | ||||||||||||||||||||||||||||||||
Divisions: | Unspecified | ||||||||||||||||||||||||||||||||
Subjects: | no entry | ||||||||||||||||||||||||||||||||
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URI: | http://kups.ub.uni-koeln.de/id/eprint/58447 |
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