Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2020

Abstract

Motivation: Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting. Results: In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while finegrained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods. Availability and implementation: DDGCN is implemented in Python 3.7, open-source and freely available at https://github.com/CXX1113/Dual-DropoutGCN. Supplementary information: Supplementary data are available at Bioinformatics online.

Keywords

synthetic lethality, graph convolutional networks, dual dropout

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Bioinformatics

Volume

36

Issue

16

First Page

4458

Last Page

4465

ISSN

1367-4803

Identifier

10.1093/bioinformatics/btaa211

Publisher

Oxford University Press (OUP): Policy B - Oxford Open Option B

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1093/bioinformatics/btaa211

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