Prediction of Synthetic Lethal Interactions in Human Cancers using Multi-view Graph Auto-Encoder

Prediction of Synthetic Lethal Interactions in Human Cancers using Multi-view Graph Auto-Encoder
Title:
Prediction of Synthetic Lethal Interactions in Human Cancers using Multi-view Graph Auto-Encoder
Other Titles:
IEEE Journal of Biomedical and Health Informatics
DOI:
10.1109/JBHI.2021.3079302
Keywords:
Publication Date:
11 May 2021
Citation:
Hao, Z., Wu, D., Fang, Y., Wu, M., Cai, R., & Li, X. (2021). Prediction of Synthetic Lethal Interactions in Human Cancers using Multi-view Graph Auto-Encoder. IEEE Journal of Biomedical and Health Informatics.
Abstract:
Synthetic lethality (SL) is a very important concept for the development of targeted anticancer drugs. However, experimental methods for SL detection often suffer from various issues like high cost and low consistency across cell lines. Hence, computational methods for predicting novel SLs have recently emerged as complements for wet-lab experiments. In addition, SL data can be represented as a graph where nodes are genes and edges are the SL interactions. It is thus motivated to design advanced graph-based machine learning algorithms for SL prediction. In this paper, we propose a novel SL prediction method using Multi-view Graph Auto-Encoder (SLMGAE). We consider the SL graph as main view and the graphs from other data sources (e.g., PPI, GO, etc.) as support views. Multiple Graph Auto-Encoders (GAEs) are implemented to reconstruct the graphs for different views. We further design an attention mechanism, which assigns the weights for support views, to combine all the reconstructed graphs for SL prediction. The overall SLMGAE model is then trained by minimizing both the reconstruction error and prediction error. Experimental results on the SynLethDB dataset show that SLMGAE outperforms state-of-the-arts. The case studies on novel predicted SLs also illustrate the effectiveness of our SLMGAE method. In addition, the source codes, data and supplementary materials for our SLMGAE are available via https://github.com/DiNg1011/SLMGAE.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Natural Science Foundation of China - -
Grant Reference no. : 61876043

This research / project is supported by the Natural Science Foundation of Guangdong - -
Grant Reference no. : 2014A030306004 and 2014A030308008

This research / project is supported by the Guangdong High-level Personnel of Special Support Program - -
Grant Reference no. : 2015TQ01X140

This research / project is supported by the Science and Technology Planning Project of Guangzhou - -
Grant Reference no. : 201902010058
Description:
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ISSN:
2168-2208
2168-2194
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