Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2022

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 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.

Discipline

Data Storage Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Bioinformatics

Volume

38

Issue

5

First Page

2254

Last Page

2262

ISSN

1367-4803

Identifier

10.1093/bioinformatics/btac100

Publisher

Oxford University Press (OUP): Policy B - Oxford Open Option B

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