Universität zu Köln

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

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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
Creators:	Creators Email ORCID ORCID Put Code Danial, John S. H. UNSPECIFIED UNSPECIFIED UNSPECIFIED Shalaby, Raed UNSPECIFIED UNSPECIFIED UNSPECIFIED Cosentino, Katia UNSPECIFIED UNSPECIFIED UNSPECIFIED Mahmoud, Marwa M. UNSPECIFIED UNSPECIFIED UNSPECIFIED Medhat, Fady UNSPECIFIED UNSPECIFIED UNSPECIFIED Klenerman, David UNSPECIFIED UNSPECIFIED UNSPECIFIED Saez, Ana J. Garcia UNSPECIFIED UNSPECIFIED UNSPECIFIED
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
Uncontrolled Keywords:	Keywords Language MICROSCOPY Multiple languages Biochemical Research Methods; Biotechnology & Applied Microbiology; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Statistics & Probability Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58447