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Identification of C. elegans Strains Using a Fully Convolutional Neural Network on Behavioural Dynamics

  • Avelino JaverEmail author
  • André E. X. Brown
  • Iasonas Kokkinos
  • Jens Rittscher
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)

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.

Keywords

Behavioural phenotyping Classification Deep learning 

Notes

Acknowledgments

This work was supported by grants EPSRC SeeBiByte Programme EP/M013774/1 to JR and Medical Research Council MC-A658-5TY30 to AEXB. AJ benefited from both grants.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Avelino Javer
    • 1
    • 2
    Email author
  • André E. X. Brown
    • 3
    • 4
  • Iasonas Kokkinos
    • 5
  • Jens Rittscher
    • 1
    • 2
  1. 1.Institute of Biomedical Engineering, Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Big Data InstituteUniversity of OxfordOxfordUK
  3. 3.MRC London Institute of Medical SciencesLondonUK
  4. 4.Institute of Clinical SciencesImperial College LondonLondonUK
  5. 5.Facebook AI ResearchParisFrance

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