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
ISSN1367-4803
EISSN1460-2059
卷号37期号:16页码:2432-2440
DOI10.1093/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.
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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
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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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