https://ieeexplore.ieee.org/abstract/document/10385796

Integrating Multi-omics Data into A Gated Graph Convolutional Networks for Identifying Cancer Driver Genes and Function Modules

Publisher: IEEE

Qianqian Peng; Zhihan He; Shichao Liu; Xinzhi Yao; Jingbo Xia

Abstract:

The identification of cancer driver genes is important for better understanding the hallmarks of cancer and developing precision therapies. Though the integration of multiomics and protein-protein interaction (PPI) data into graph convolutional networks (GCN) has been emerging as a promising strategy, these GCN-based methods rely heavily on the reliability and completeness of the PPI network data, thereby hampering the identification of pan-cancer genes and key oncogenic interactions. Furthermore, few GCN-based models today enables the detection of function modules and driver genes in a simultaneous manner. To this end, we introduce a novel GCN-based model, “Gated graph convolutional network with ATtention mechanism for identifying cancer Driver Genes and function modules (DGGAT)”. This model integrates the gating mechanisms with Gumbel-Softmax re-parameterization and attention mechanisms, aiming to control which interactions participate in the flow of information in PPI and weight the interactions. We apply DGGAT to identify novel cancer genes and function modules through five PPI networks. Comparison results of our model with baseline models indicated that our model obtain the highest accuracy in cancer genes identification. Further case study demonstrated that the model possesses the capability to unveil the molecular mechanism with its inherent function modules.

Published in: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Date of Conference: 05-08 December 2023

Date Added to IEEE Xplore: 18 January 2024

ISBN Information:

ISSN Information:

DOI: 10.1109/BIBM58861.2023.10385796Publisher: IEEE

Conference Location: Istanbul, Turkiye