Journal article
MTGNN: Multi-Task Graph Neural Network based few-shot learning for disease similarity measurement
Methods (San Diego, Calif.), v 198, pp 88-95
Feb 2022
PMID: 34700014
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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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Details
- Title
- MTGNN: Multi-Task Graph Neural Network based few-shot learning for disease similarity measurement
- Creators
- Jianliang Gao - Central South UniversityXiangchi Zhang - Central South UniversityLing Tian - Central South UniversityYuxin Liu - Central South UniversityJianxin Wang - Central South UniversityZhao Li - Alibaba GroupXiaohua Hu - Drexel University
- Publication Details
- Methods (San Diego, Calif.), v 198, pp 88-95
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000746136400010
- Scopus ID
- 2-s2.0-85118727875
- Other Identifier
- 991019167609704721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biochemistry & Molecular Biology