Graph contextualized attention network for predicting synthetic lethality in human cancers

Yahui Long; Min Wu; Yong Liu; Jie Zheng; Chee Keong Kwoh; Jiawei Luo; Xiaoli Li

doi:10.1093/bioinformatics/btab110

Graph contextualized attention network for predicting synthetic lethality in human cancers

Bioinformatics. 2021 Aug 25;37(16):2432-2440. doi: 10.1093/bioinformatics/btab110.

Authors

Yahui Long^{1

2}, Min Wu³, Yong Liu⁴, Jie Zheng⁵, Chee Keong Kwoh², Jiawei Luo¹, Xiaoli Li³

Affiliations

¹ College of Computer Science and Electronic Engineering, Hunan University, Changsha 410000, China.
² School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore.
³ Machine Intellection Department, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.
⁴ Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore 639798, Singapore.
⁵ School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China.

PMID: 33609108
DOI: 10.1093/bioinformatics/btab110

Abstract

Motivation: Synthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental screening of candidate SL pairs. Several matrix factorization-based methods have recently been proposed for human SL prediction. However, they are limited in capturing the dependencies of neighbors. In addition, it is also highly challenging to make accurate predictions for new genes without any known SL partners.

Results: In this work, we propose a novel graph contextualized attention network named GCATSL to learn gene representations for SL prediction. First, we leverage different data sources to construct multiple feature graphs for genes, which serve as the feature inputs for our GCATSL method. Second, for each feature graph, we design node-level attention mechanism to effectively capture the importance of local and global neighbors and learn local and global representations for the nodes, respectively. We further exploit multi-layer perceptron (MLP) to aggregate the original features with the local and global representations and then derive the feature-specific representations. Third, to derive the final representations, we design feature-level attention to integrate feature-specific representations by taking the importance of different feature graphs into account. Extensive experimental results on three datasets under different settings demonstrated that our GCATSL model outperforms 14 state-of-the-art methods consistently. In addition, case studies further validated the effectiveness of our proposed model in identifying novel SL pairs.

Availabilityand implementation: Python codes and dataset are freely available on GitHub (https://github.com/longyahui/GCATSL) and Zenodo (https://zenodo.org/record/4522679) under the MIT license.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

Grants and funding