Journal
METHODS
Volume 198, Issue -, Pages 88-95Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.ymeth.2021.10.005
Keywords
Disease similarity; Multi-Task Graph Neural Network; Link prediction; Few-shot learning
Funding
- National Natural Science Foundation of China [61873288]
- Fundamental Research Funds for the Central Universities of Central South University [2020zzts586]
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This paper proposes a Multi-Task Graph Neural Network (MTGNN) framework for measuring disease similarity using few-shot learning, tackling the problem of insufficient labeled similar disease pairs.
Similar diseases are usually caused by molecular origins or similar phenotypes. Confirming the relationship between diseases can help researchers gain a deep insight of the pathogenic mechanisms of emerging complex diseases, and improve the corresponding diagnoses and treatment. Therefore, similar diseases are considerably important in biology and pathology. However, the insufficient number of labelled similar disease pairs cannot support the optimal training of the models. In this paper, we propose a Multi-Task Graph Neural Network (MTGNN) framework to measure disease similarity by few-shot learning. To tackle the problem of insufficient number of labelled similar disease pairs, we design the multi-task optimization strategy to train the graph neural network for disease similarity task (lack of labelled training data) by introducing link prediction task (sufficient labelled training data). The similarity between diseases can then be obtained by measuring the distance between disease embeddings in high-dimensional space learning from the double tasks. The experiment results evaluate the performance of MTGNN and illustrate its advantages over previous methods on few labeled training dataset.
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