JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2023, Vol. 58 ›› Issue (12): 22-30.doi: 10.6040/j.issn.1671-9352.1.2022.8766

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Label guided multi-scale graph neural network for protein-protein interactions prediction

Xinsheng WANG(),Xiaofei ZHU*(),Chenghong LI   

  1. School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
  • Received:2022-09-29 Online:2023-12-20 Published:2023-12-19
  • Contact: Xiaofei ZHU E-mail:wxscc0610@2020.cqut.edu.cn;zxf@cqut.edu.cn

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Abstract:

A protein-protein interactions prediction method based on label guided multi-scale graph neural network is proposed, which not only enhances the representation ability of data, but also introduces label information to guide learning. Firstly, the multi-scale graph representation is obtained by graph data augmentation, and the multi-scale graph representation is input into graph neural network to obtain multi-scale protein representation, and comparative learning is introduced to further improve the protein characterization ability. Secondly, the self-learning label relation graph is constructed to learn the relationship between labels and obtain the feature representation of labels. Finally, the prediction of protein-protein interactions is guided by the feature representation of labels. Experiments are carried out on three public datasets. Compared with the optimal benchmark method, the proposed method has better performance. Specifically, compared with the best baseline method, the micro-F1 scores on the three datasets SHS27k, SHS148k and STRING increase by 2.01%, 0.94% and 0.93% respectively.

Key words: protein-protein interactions, graph neural network, graph data augmentation, graph relation graph

CLC Number: 

  • TP391

Fig.1

Framework of model LGMG-PPI"

Table 1

The statistics of datasets"

数据集 节点数 连边数 氨基酸数(Avg) 标签数
SHS27k 1 690 7 624 571 7
SHS148k 5 189 44 488 597 7
STRING 15 335 593 397 604 7

Table 2

The micro-F1 of different models on different datasets  单位: %"

方法 SHS27k SHS148k STRING
SVM 75.35±1.05 80.55±0.23 —
RF 78.45±0.88 82.10±0.20 88.91±0.08
LR 71.55±0.93 67.00±0.07 67.74±0.16
DPPI 73.99±5.04 77.48±1.39 94.85±0.13
DNN-PPI 77.89±4.97 88.49±0.48 83.08±0.11
PIPR 83.31±0.75 90.05±2.59 94.43±0.10
GNN-PPI 87.91±0.39 92.26±0.10 95.43±0.10
LGMG-PPI 89.68±0.10 93.13±0.03 96.32±0.04

Table 3

The ablation study  单位: %"

方法 SHS27k SHS148k STRING
LGMG-PPI 89.68±0.10 93.13±0.03 96.32±0.04
w/o MS-GDA($\mathscr{T}_{1}$) 89.35±0.05 93.05±0.11 96.14±0.17
w/o MS-GDA($\mathscr{T}_{2}$) 89.23±0.10 92.95±0.04 96.28±0.17
w/o MS-GDA 88.97±0.09 92.80±0.14 95.92±0.03
w/o SL-LRG 89.39±0.12 92.87±0.04 96.04±0.02

Fig.2

Verify the validity of topology structure of SL-LRG"

Fig.3

Verify the validity of node feature of SL-LRG"

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