Zeng, Z., Zhao, S., Da, Q., Qian, P., Leong, T. W., Dai, L., Nordlund, P., Prabhu, N., Zhao, Z., & Yang, X. (2022). CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). https://doi.org/10.1109/embc48229.2022.9871875
Abstract:
Cellular Thermal Shift Assay (CETSA) has been
widely used in drug discovery, cancer cell biology, immunology,
etc. One of the barriers for CETSA applications is that CETSA
experiments have to be conducted on various cell lines, which
is extremely time-consuming and costly. In this study, we make
an effort to explore the translation of CETSA features cross
cell lines, i.e., known CETSA feature of a given protein in
one cell line, can we automatically predict the CETSA feature
of this protein in another cell line, and vice versa? Inspired
by pix2pix and CycleGAN, which perform well on image-toimage
translation cross various domains in computer vision, we
propose a novel deep neural network model called CycleDNN
for CETSA feature translation cross cell lines. Given cell lines A
and B, the proposed CycleDNN consists of two auto-encoders,
the first one encodes the CETSA feature from cell line A into Z
in the latent space Z, then decodes Z into the CETSA feature
in cell line B. Similarly, the second one translates the CETSA
feature from cell line B to cell line A through the latent space
Z′. In such a way, the two auto-encoders form a cyclic feature
translation between cell lines. The reconstructed loss, cycleconsistency
loss, and latent vector regularization loss are used
to guide the training of the model. The experimental results on
a public CETSA dataset demonstrate the effectiveness of the
proposed approach.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation - Competitive Research Programme
Grant Reference no. : NRFCRP22- 2019-0003