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SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer
Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8047129/ https://www.ncbi.nlm.nih.gov/pubmed/33867966 http://dx.doi.org/10.3389/fnbot.2021.665055 |
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author | Ma, Yugang Li, Qing Hu, Nan Li, Lili |
author_facet | Ma, Yugang Li, Qing Hu, Nan Li, Lili |
author_sort | Ma, Yugang |
collection | PubMed |
description | Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods in sustainable development and advanced manufacturing. To date, most manufacturing graph neural networks are mainly evaluated on social and information networks, which improve the quality of network representation y integrating neighbor node descriptions. However, previous methods have not yet been comprehensively studied on biomedical networks. Traditional techniques fail to achieve satisfying results, especially when labeled nodes are deficient in number. In this paper, a new semi-supervised deep learning method for the biomedical graph via sustainable knowledge transfer called SeBioGraph is proposed. In SeBioGraph, both node embedding and graph-specific prototype embedding are utilized as transferable metric space characterized. By incorporating prior knowledge learned from auxiliary graphs, SeBioGraph further promotes the performance of the target graph. Experimental results on the two-class node classification tasks and three-class link prediction tasks demonstrate that the SeBioGraph realizes state-of-the-art results. Finally, the method is thoroughly evaluated. |
format | Online Article Text |
id | pubmed-8047129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-80471292021-04-16 SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer Ma, Yugang Li, Qing Hu, Nan Li, Lili Front Neurorobot Neuroscience Semi-supervised deep learning for the biomedical graph and advanced manufacturing graph is rapidly becoming an important topic in both academia and industry. Many existing types of research focus on semi-supervised link prediction and node classification, as well as the application of these methods in sustainable development and advanced manufacturing. To date, most manufacturing graph neural networks are mainly evaluated on social and information networks, which improve the quality of network representation y integrating neighbor node descriptions. However, previous methods have not yet been comprehensively studied on biomedical networks. Traditional techniques fail to achieve satisfying results, especially when labeled nodes are deficient in number. In this paper, a new semi-supervised deep learning method for the biomedical graph via sustainable knowledge transfer called SeBioGraph is proposed. In SeBioGraph, both node embedding and graph-specific prototype embedding are utilized as transferable metric space characterized. By incorporating prior knowledge learned from auxiliary graphs, SeBioGraph further promotes the performance of the target graph. Experimental results on the two-class node classification tasks and three-class link prediction tasks demonstrate that the SeBioGraph realizes state-of-the-art results. Finally, the method is thoroughly evaluated. Frontiers Media S.A. 2021-04-01 /pmc/articles/PMC8047129/ /pubmed/33867966 http://dx.doi.org/10.3389/fnbot.2021.665055 Text en Copyright © 2021 Ma, Li, Hu and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Ma, Yugang Li, Qing Hu, Nan Li, Lili SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer |
title | SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer |
title_full | SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer |
title_fullStr | SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer |
title_full_unstemmed | SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer |
title_short | SeBioGraph: Semi-supervised Deep Learning for the Graph via Sustainable Knowledge Transfer |
title_sort | sebiograph: semi-supervised deep learning for the graph via sustainable knowledge transfer |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8047129/ https://www.ncbi.nlm.nih.gov/pubmed/33867966 http://dx.doi.org/10.3389/fnbot.2021.665055 |
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