Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162781
Title: Pre-training graph neural networks for link prediction in biomedical networks
Authors: Long, Yahui
Wu, Min
Liu, Yong
Fang, Yuan
Kwoh, Chee Keong
Chen, Jinmiao
Luo, Jiawei
Li, Xiaoli
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Long, Y., Wu, M., Liu, Y., Fang, Y., Kwoh, C. K., Chen, J., Luo, J. & Li, X. (2022). Pre-training graph neural networks for link prediction in biomedical networks. Bioinformatics, 38(8), 2254-2262. https://dx.doi.org/10.1093/bioinformatics/btac100
Journal: Bioinformatics
Abstract: Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g. proteins, drugs, etc.) for biomedical applications. Predicting potential interactions/links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been utilized for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g. sequence, structure and network data. However, it is challenging to effectively integrate these data sources and automatically extract features for different link prediction tasks. Results: In this article, we propose a novel Pre-Training Graph Neural Networks-based framework named PT-GNN to integrate different data sources for link prediction in biomedical networks. First, we design expressive deep learning methods [e.g. convolutional neural network and graph convolutional network (GCN)] to learn features for individual nodes from sequence and structure data. Second, we further propose a GCN-based encoder to effectively refine the node features by modelling the dependencies among nodes in the network. Third, the node features are pre-trained based on graph reconstruction tasks. The pre-trained features can be used for model initialization in downstream tasks. Extensive experiments have been conducted on two critical link prediction tasks, i.e. synthetic lethality (SL) prediction and drug–target interaction (DTI) prediction. Experimental results demonstrate PT-GNN outperforms the state-of-the-art methods for SL prediction and DTI prediction. In addition, the pre-trained features benefit improving the performance and reduce the training time of existing models. Availability and implementation: Python codes and dataset are available at: https://github.com/longyahui/PT-GNN.
URI: https://hdl.handle.net/10356/162781
ISSN: 1367-4803
DOI: 10.1093/bioinformatics/btac100
Rights: © The Author(s) 2022. Published by Oxford University Press. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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