NPI-DCGNN: An Accurate Tool for Identifying ncRNA-Protein Interactions Using a Dual-Channel Graph Neural Network

被引：1
|
作者：
Zhang, Xin [1 ]
Zhao, Liangwei [1 ]
Chai, Ziyi [1 ]
Wu, Hao [2 ]
Yang, Wei [3 ]
Li, Chen [4 ,5 ]
Jiang, Yu [6 ]
Liu, Quanzhong [1 ]
机构：
[1] Northwest A&F Univ, Coll Informat Engn, 3 Taicheng Rd, Yangling 712100, Peoples R China
[2] Shandong Univ, Sch Software, Jinan, Peoples R China
[3] Natl Clin Res Ctr Infect Dis, Shenzhen, Peoples R China
[4] Monash Univ, Monash Biomed Discovery Inst, 15 Innovat Walk Clayton Campus, Melbourne, Vic 3800, Australia
[5] Monash Univ, Dept Biochem & Mol Biol, 15 Innovat Walk Clayton Campus, Melbourne, Vic 3800, Australia
[6] Northwest A&F Univ, Coll Anim Sci & Technol, Key Lab Anim Genet Breeding & Reprod Shaanxi Prov, Yangling 712100, Peoples R China
来源：
JOURNAL OF COMPUTATIONAL BIOLOGY | 2024年 / 31卷 / 08期
基金：
中国国家自然科学基金; 国家重点研发计划;
关键词：
graph neural network; noncoding RNA; ncRNA-protein interactions; dual-channel; NONCODING RNAS; IDENTIFICATION; SPECIFICITIES; SEQUENCE; DATABASE;
D O I：
10.1089/cmb.2023.0449
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
Noncoding RNA (NcRNA)-protein interactions (NPIs) play fundamentally important roles in carrying out cellular activities. Although various predictors based on molecular features and graphs have been published to boost the identification of NPIs, most of them often ignore the information between known NPIs or exhibit insufficient learning ability from graphs, posing a significant challenge in effectively identifying NPIs. To develop a more reliable and accurate predictor for NPIs, in this article, we propose NPI-DCGNN, an end-to-end NPI predictor based on a dual-channel graph neural network (DCGNN). NPI-DCGNN initially treats the known NPIs as an ncRNA-protein bipartite graph. Subsequently, for each ncRNA-protein pair, NPI-DCGNN extracts two local subgraphs centered around the ncRNA and protein, respectively, from the bipartite graph. After that, it utilizes a dual-channel graph representation learning layer based on GNN to generate high-level feature representations for the ncRNA-protein pair. Finally, it employs a fully connected network and output layer to predict whether an interaction exists between the pair of ncRNA and protein. Experimental results on four experimentally validated datasets demonstrate that NPI-DCGNN outperforms several state-of-the-art NPI predictors. Our case studies on the NPInter database further demonstrate the prediction power of NPI-DCGNN in predicting NPIs. With the availability of the source codes (https://github.com/zhangxin11111/NPI-DCGNN), we anticipate that NPI-DCGNN could facilitate the studies of ncRNA interactome by providing highly reliable NPI candidates for further experimental validation.
引用
收藏
页码：742 / 756
页数：15
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