Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers
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 approac...
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sg-smu-ink.sis_research-61302021-05-24T03:00:28Z Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers CAI, Ruichu CHEN, Xuexin FANG, Yuan WU, Min HAO, Yuexing 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. 2020-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5127 info:doi/10.1093/bioinformatics/btaa211 https://ink.library.smu.edu.sg/context/sis_research/article/6130/viewcontent/BIOINFO20_DDGCN_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University synthetic lethality graph convolutional networks dual dropout Databases and Information Systems OS and Networks |
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synthetic lethality graph convolutional networks dual dropout Databases and Information Systems OS and Networks CAI, Ruichu CHEN, Xuexin FANG, Yuan WU, Min HAO, Yuexing Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers |
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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. |
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CAI, Ruichu CHEN, Xuexin FANG, Yuan WU, Min HAO, Yuexing |
author_facet |
CAI, Ruichu CHEN, Xuexin FANG, Yuan WU, Min HAO, Yuexing |
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CAI, Ruichu |
title |
Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers |
title_short |
Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers |
title_full |
Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers |
title_fullStr |
Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers |
title_full_unstemmed |
Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers |
title_sort |
dual-dropout graph convolutional network for predicting synthetic lethality in human cancers |
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Institutional Knowledge at Singapore Management University |
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2020 |
url |
https://ink.library.smu.edu.sg/sis_research/5127 https://ink.library.smu.edu.sg/context/sis_research/article/6130/viewcontent/BIOINFO20_DDGCN_av.pdf |
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