Advertisement

Characterizing Phenotype Abnormality by Variational Auto Encoder

  • Yuki KimuraEmail author
  • Takaya Ueda
  • Seo Masataka
  • Yukako Tohsato
  • Ikuko Nishikawa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

Variational auto-encoder (VAE) acquires not only a low dimensional representation of the data, but also the probability distribution of the data, in its latent space. Therefore, if an input data is not from the trained category, it fails to find an appropriate point in the latent space, which leads to a poor reconstruction. Thus, it can be used for data abnormality detection. We use VAE to detect a phenotype abnormality in a biological system. When each of lethal genes in early embryogenesis is knocked down by RNAi technique, an observed abnormality can be linked to the corresponding gene function. We train VAE by the wild type (WT) data without any gene manipulation, and use to characterize a phenotype of RNAi embryo in two cell stage. Abnormality is defined by a data reconstruction error, and several genes are found whose absence causes the abnormality, including some already known genes.

Keywords

Variational auto encoder Anomaly detection Latent space Biological dynamics data 

References

  1. 1.
    Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)Google Scholar
  2. 2.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998)CrossRefGoogle Scholar
  3. 3.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Kyoda, K., et al.: WDDD: worm developmental dynamics database. Nucleic Acids Res. 41, D732–D737 (2013)CrossRefGoogle Scholar
  5. 5.
    Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: Deconvolutional networks. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2528–2535 (2010)Google Scholar
  6. 6.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yuki Kimura
    • 1
    Email author
  • Takaya Ueda
    • 1
  • Seo Masataka
    • 1
  • Yukako Tohsato
    • 1
  • Ikuko Nishikawa
    • 1
  1. 1.Ritsumeikan UniversityKusatsuJapan

Personalised recommendations