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Conference proceeding
Multi-task Based Few-Shot Learning for Disease Similarity Measurement
2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 263-268
16 Dec 2020
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Abstract
To identify and explore the similarities between diseases is of great significance for u nderstanding t he pathogenic mechanisms of emerging complex diseases. Some methods try to measure the similarity of diseases through deep learning models. 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 retrieve similar diseases by few-shot learning. To deal with the problem of insufficient n umber o f labelled similar disease pairs, we design double tasks to optimize 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 illustrate the overall effectiveness by comparing with prior methods on few labeled training dataset.
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Details
- Title
- Multi-task Based Few-Shot Learning for Disease Similarity Measurement
- Creators
- Jianliang Gao - Central South UniversityLing Tian - Central South UniversityYuxin Liu - Central South UniversityJianxin Wang - Central South UniversityZhao Li - Alibaba GroupXiaohua Hu - Drexel University
- Publication Details
- 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp 263-268
- Publisher
- IEEE
- Grant note
- CERN (10.13039/100012470) National Natural Science Foundation of China (10.13039/501100001809)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000659487100048
- Scopus ID
- 2-s2.0-85100333825
- Other Identifier
- 991019167598404721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology