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.
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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 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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