Pre-training graph neural networks for link prediction in biomedical networks

Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential links in biomedical networks is important for understanding the pathological mechanisms of various complex human dise...

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Main Authors: LONG, Yahui, WU, Min, LIU, Yong, FANG, Yuan, KWOH, Chee Kong, LUO, Jiawei, LI, Xiaoli
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Language:English
Published: Institutional Knowledge at Singapore Management University 2022
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Online Access:https://ink.library.smu.edu.sg/sis_research/7158
https://ink.library.smu.edu.sg/context/sis_research/article/8161/viewcontent/BIBM.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-81612022-05-12T04:26:50Z Pre-training graph neural networks for link prediction in biomedical networks LONG, Yahui WU, Min LIU, Yong FANG, Yuan KWOH, Chee Kong LUO, Jiawei LI, Xiaoli Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential 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 designed 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 paper, we propose a novel pre-training model to integrate different data sources for link prediction in biomedical networks. First, we design expressive deep learning methods (e.g., CNN and GCN) to learn features for individual nodes from sequence and structure data. Second, we further propose a GCN-based encoder to effectively refine the features of nodes by modelling the dependencies among nodes in the network data. Third, the model is pre-trained based on graph reconstruction 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 that the features generated by our pre-training model can help to improve the performance and reduce the training time for existing GNN models. In addition, fine-tuning the pre-trained model to a specific task can also achieve the performance comparable to the state-of-the-art methods. 2022-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/7158 info:doi/10.1093/bioinformatics/btac100 https://ink.library.smu.edu.sg/context/sis_research/article/8161/viewcontent/BIBM.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Data Storage Systems OS and Networks
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Data Storage Systems
OS and Networks
spellingShingle Data Storage Systems
OS and Networks
LONG, Yahui
WU, Min
LIU, Yong
FANG, Yuan
KWOH, Chee Kong
LUO, Jiawei
LI, Xiaoli
Pre-training graph neural networks for link prediction in biomedical networks
description Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential 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 designed 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 paper, we propose a novel pre-training model to integrate different data sources for link prediction in biomedical networks. First, we design expressive deep learning methods (e.g., CNN and GCN) to learn features for individual nodes from sequence and structure data. Second, we further propose a GCN-based encoder to effectively refine the features of nodes by modelling the dependencies among nodes in the network data. Third, the model is pre-trained based on graph reconstruction 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 that the features generated by our pre-training model can help to improve the performance and reduce the training time for existing GNN models. In addition, fine-tuning the pre-trained model to a specific task can also achieve the performance comparable to the state-of-the-art methods.
format text
author LONG, Yahui
WU, Min
LIU, Yong
FANG, Yuan
KWOH, Chee Kong
LUO, Jiawei
LI, Xiaoli
author_facet LONG, Yahui
WU, Min
LIU, Yong
FANG, Yuan
KWOH, Chee Kong
LUO, Jiawei
LI, Xiaoli
author_sort LONG, Yahui
title Pre-training graph neural networks for link prediction in biomedical networks
title_short Pre-training graph neural networks for link prediction in biomedical networks
title_full Pre-training graph neural networks for link prediction in biomedical networks
title_fullStr Pre-training graph neural networks for link prediction in biomedical networks
title_full_unstemmed Pre-training graph neural networks for link prediction in biomedical networks
title_sort pre-training graph neural networks for link prediction in biomedical networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2022
url https://ink.library.smu.edu.sg/sis_research/7158
https://ink.library.smu.edu.sg/context/sis_research/article/8161/viewcontent/BIBM.pdf
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