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KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers

MOTIVATION: Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-...

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Autores principales: Wang, Shike, Xu, Fan, Li, Yunyang, Wang, Jie, Zhang, Ke, Liu, Yong, Wu, Min, Zheng, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336442/
https://www.ncbi.nlm.nih.gov/pubmed/34252965
http://dx.doi.org/10.1093/bioinformatics/btab271
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author Wang, Shike
Xu, Fan
Li, Yunyang
Wang, Jie
Zhang, Ke
Liu, Yong
Wu, Min
Zheng, Jie
author_facet Wang, Shike
Xu, Fan
Li, Yunyang
Wang, Jie
Zhang, Ke
Liu, Yong
Wu, Min
Zheng, Jie
author_sort Wang, Shike
collection PubMed
description MOTIVATION: Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-based data mining and machine learning methods. Most of the existing methods tend to assume that SL pairs are independent of each other, without taking into account the shared biological mechanisms underlying the SL pairs. Although several methods have incorporated genomic and proteomic data to aid SL prediction, these methods involve manual feature engineering that heavily relies on domain knowledge. RESULTS: Here, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL. The integration of KG can help harness the independence issue and circumvent manual feature engineering by conducting message-passing on the KG. Our model outperformed all the state-of-the-art baselines in area under the curve, area under precision-recall curve and F1. Extensive experiments, including the comparison of our model with an unsupervised TransE model, a vanilla graph convolutional network model, and their combination, demonstrated the significant impact of incorporating KG into GNN for SL prediction. AVAILABILITY AND IMPLEMENTATION: : KG4SL is freely available at https://github.com/JieZheng-ShanghaiTech/KG4SL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-83364422021-08-09 KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers Wang, Shike Xu, Fan Li, Yunyang Wang, Jie Zhang, Ke Liu, Yong Wu, Min Zheng, Jie Bioinformatics Systems Biology and Networks MOTIVATION: Synthetic lethality (SL) is a promising gold mine for the discovery of anti-cancer drug targets. Wet-lab screening of SL pairs is afflicted with high cost, batch-effect, and off-target problems. Current computational methods for SL prediction include gene knock-out simulation, knowledge-based data mining and machine learning methods. Most of the existing methods tend to assume that SL pairs are independent of each other, without taking into account the shared biological mechanisms underlying the SL pairs. Although several methods have incorporated genomic and proteomic data to aid SL prediction, these methods involve manual feature engineering that heavily relies on domain knowledge. RESULTS: Here, we propose a novel graph neural network (GNN)-based model, named KG4SL, by incorporating knowledge graph (KG) message-passing into SL prediction. The KG was constructed using 11 kinds of entities including genes, compounds, diseases, biological processes and 24 kinds of relationships that could be pertinent to SL. The integration of KG can help harness the independence issue and circumvent manual feature engineering by conducting message-passing on the KG. Our model outperformed all the state-of-the-art baselines in area under the curve, area under precision-recall curve and F1. Extensive experiments, including the comparison of our model with an unsupervised TransE model, a vanilla graph convolutional network model, and their combination, demonstrated the significant impact of incorporating KG into GNN for SL prediction. AVAILABILITY AND IMPLEMENTATION: : KG4SL is freely available at https://github.com/JieZheng-ShanghaiTech/KG4SL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-07-12 /pmc/articles/PMC8336442/ /pubmed/34252965 http://dx.doi.org/10.1093/bioinformatics/btab271 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Systems Biology and Networks
Wang, Shike
Xu, Fan
Li, Yunyang
Wang, Jie
Zhang, Ke
Liu, Yong
Wu, Min
Zheng, Jie
KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
title KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
title_full KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
title_fullStr KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
title_full_unstemmed KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
title_short KG4SL: knowledge graph neural network for synthetic lethality prediction in human cancers
title_sort kg4sl: knowledge graph neural network for synthetic lethality prediction in human cancers
topic Systems Biology and Networks
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336442/
https://www.ncbi.nlm.nih.gov/pubmed/34252965
http://dx.doi.org/10.1093/bioinformatics/btab271
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