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Identification of C. elegans strains using a fully convolutional neural network on behavioural dynamics

Javer, A; Brown, AEX; Kokkinos, I; Rittscher, J; (2018) Identification of C. elegans strains using a fully convolutional neural network on behavioural dynamics. In: Leal-Taixé, L and Roth, S, (eds.) Computer Vision – ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part VI. (pp. pp. 455-464). Springer Nature: Cham, Switzerland. Green open access

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Abstract

The nematode C. elegans is a promising model organism to understand the genetic basis of behaviour due to its anatomical simplicity. In this work, we present a deep learning model capable of discerning genetically diverse strains based only on their recorded spontaneous activity, and explore how its performance changes as different embeddings are used as input. The model outperforms hand-crafted features on strain classification when trained directly on time series of worm postures.

Type: Proceedings paper
Title: Identification of C. elegans strains using a fully convolutional neural network on behavioural dynamics
Event: European Conference on Computer Vision 2018
ISBN-13: 9783030110239
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-11024-6_35
Publisher version: https://doi.org/10.1007/978-3-030-11024-6_35
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Behavioural phenotyping, Classification, Deep learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10075048
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