PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers

doi:10.1093/bioinformatics/btac476

	PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers
	Liu, Xin1 ; Yu, Jiale1 ; Tao, Siyu1 ; Yang, Beiyuan1 ; Wang, Shike1 ; Wang, Lin2,3 ; Bai, Fang1,2,3 ; Zheng, Jie1,4
	2022-09-16
发表期刊	BIOINFORMATICS
ISSN	1367-4803
EISSN	1367-4811
卷号	38
发表状态	已发表
DOI	10.1093/bioinformatics/btac476
摘要	Motivation: Synthetic lethality (SL) is a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect the cell viability. It can effectively expand the range of anti-cancer therapeutic targets. SL interactions are identified mainly by experimental screening and computational prediction. Recent machine-learning methods mostly learn the representation of each gene individually, ignoring the representation of the pairwise interaction between two genes. In addition, the mechanisms of SL, the key to translating SL into cancer therapeutics, are often unclear. Results: To fill the gaps, we propose a pairwise interaction learning-based graph neural network (GNN) named PiLSL to learn the representation of pairwise interaction between two genes for SL prediction. First, we construct an enclosing graph for each pair of genes from a knowledge graph. Secondly, we design an attentive embedding propagation layer in a GNN to discriminate the importance among the edges in the enclosing graph and to learn the latent features of the pairwise interaction from the weighted enclosing graph. Finally, we further fuse the latent features with explicit features extracted from multi-omics data to obtain powerful gene representations for SL prediction. Extensive experimental results demonstrate that PiLSL outperforms the best baseline by a large margin and generalizes well under three realistic scenarios. Besides, PiLSL provides an explanation of SL mechanisms via the weighted paths in the enclosing graphs by attention mechanism.
URL	查看原文
收录类别	SCI ; 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:000864714500017
出版者	OXFORD UNIV PRESS
引用统计
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/240475
专题	免疫化学研究所_PI研究组_白芳组生命科学与技术学院_博士生信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_郑杰组
通讯作者	Zheng, Jie
作者单位	1.Shanghai Tech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 2.Shanghai Tech Univ, Sch Life Sci & Technol, Shanghai 201210, Peoples R China 3.Shanghai Tech Univ, Shanghai Inst Adv Immunochem Studies, Shanghai 201210, Peoples R China 4.Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai 201210, Peoples R China
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Liu, Xin,Yu, Jiale,Tao, Siyu,et al. PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers[J]. BIOINFORMATICS,2022,38.
APA	Liu, Xin.,Yu, Jiale.,Tao, Siyu.,Yang, Beiyuan.,Wang, Shike.,...&Zheng, Jie.(2022).PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers.BIOINFORMATICS,38.
MLA	Liu, Xin,et al."PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers".BIOINFORMATICS 38(2022).