CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines

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CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines
Title:
CycleDNN - A Novel Deep Neural Network Model for CETSA Feature Prediction cross Cell Lines
Journal Title:
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Keywords:
Publication Date:
08 September 2022
Citation:
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
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2694-0604
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