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Graph contextualized attention network for predicting synthetic lethality in human cancers | |
Long, Yahui1,2; Wu, Min3; Liu, Yong4; Zheng, Jie5; Kwoh, Chee Keong2; Luo, Jiawei1; Li, Xiaoli3 | |
2021-08-15 | |
发表期刊 | BIOINFORMATICS |
ISSN | 1367-4803 |
EISSN | 1460-2059 |
卷号 | 37期号:16页码:2432-2440 |
DOI | 10.1093/bioinformatics/btab110 |
摘要 | 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 nodelevel 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. |
URL | 查看原文 |
收录类别 | SCIE |
语种 | 英语 |
WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics |
WOS类目 | Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability |
WOS记录号 | WOS:000703909800027 |
出版者 | OXFORD UNIV PRESS |
原始文献类型 | Article |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/128344 |
专题 | 信息科学与技术学院_PI研究组_郑杰组 |
通讯作者 | Luo, Jiawei; Li, Xiaoli |
作者单位 | 1.Hunan Univ, Coll Comp Sci & Elect Engn, Changsha 410000, Peoples R China; 2.Nanyang Technol Univ, Sch Comp Sci & Engn, Singapore 639798, Singapore; 3.ASTAR, Inst Infocomm Res, Machine Intellect Dept, Singapore 138632, Singapore; 4.Nanyang Technol Univ, Joint NTU UBC Res Ctr Excellence Act Living Elder, Singapore 639798, Singapore; 5.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China |
推荐引用方式 GB/T 7714 | Long, Yahui,Wu, Min,Liu, Yong,et al. Graph contextualized attention network for predicting synthetic lethality in human cancers[J]. BIOINFORMATICS,2021,37(16):2432-2440. |
APA | Long, Yahui.,Wu, Min.,Liu, Yong.,Zheng, Jie.,Kwoh, Chee Keong.,...&Li, Xiaoli.(2021).Graph contextualized attention network for predicting synthetic lethality in human cancers.BIOINFORMATICS,37(16),2432-2440. |
MLA | Long, Yahui,et al."Graph contextualized attention network for predicting synthetic lethality in human cancers".BIOINFORMATICS 37.16(2021):2432-2440. |
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