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Discovering Interdisciplinary Research Based on Neural Networks
Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203848/ https://www.ncbi.nlm.nih.gov/pubmed/35721858 http://dx.doi.org/10.3389/fbioe.2022.908733 |
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author | He, Tao Fu, Wei Xu, Jianqiao Zhang, Zhihong Zhou, Jiuxing Yin, Ying Xie, Zhenjie |
author_facet | He, Tao Fu, Wei Xu, Jianqiao Zhang, Zhihong Zhou, Jiuxing Yin, Ying Xie, Zhenjie |
author_sort | He, Tao |
collection | PubMed |
description | Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper proposes an approach to discovering interdisciplinary research automatically. The method generates an IRD-BERT neural network model for discovering interdisciplinary research based on the pre-trained model BERT. IRD-BERT is used to simulate the domain knowledge of experts, and author keywords can be projected into vector space by this model. According to the keyword distribution in the vector space, keywords with semantic anomalies can be identified. Papers that use these author keywords are likely to be interdisciplinary research. This method is applied to discover interdisciplinary research in the deep learning research field, and its performance is better than that of similar methods. |
format | Online Article Text |
id | pubmed-9203848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92038482022-06-18 Discovering Interdisciplinary Research Based on Neural Networks He, Tao Fu, Wei Xu, Jianqiao Zhang, Zhihong Zhou, Jiuxing Yin, Ying Xie, Zhenjie Front Bioeng Biotechnol Bioengineering and Biotechnology Interdisciplinary research promotes the emergence of scientific innovation. Researchers want to find interdisciplinary research in their research field. However, the number of scientific papers published today is increasing, and completing this task by hand is time-consuming and laborious. A neural network is a machine learning model that simulates the connection mode of neurons in the human brain. It is an important application of bionics in the artificial intelligence field. This paper proposes an approach to discovering interdisciplinary research automatically. The method generates an IRD-BERT neural network model for discovering interdisciplinary research based on the pre-trained model BERT. IRD-BERT is used to simulate the domain knowledge of experts, and author keywords can be projected into vector space by this model. According to the keyword distribution in the vector space, keywords with semantic anomalies can be identified. Papers that use these author keywords are likely to be interdisciplinary research. This method is applied to discover interdisciplinary research in the deep learning research field, and its performance is better than that of similar methods. Frontiers Media S.A. 2022-06-03 /pmc/articles/PMC9203848/ /pubmed/35721858 http://dx.doi.org/10.3389/fbioe.2022.908733 Text en Copyright © 2022 He, Fu, Xu, Zhang, Zhou, Yin and Xie. 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 | Bioengineering and Biotechnology He, Tao Fu, Wei Xu, Jianqiao Zhang, Zhihong Zhou, Jiuxing Yin, Ying Xie, Zhenjie Discovering Interdisciplinary Research Based on Neural Networks |
title | Discovering Interdisciplinary Research Based on Neural Networks |
title_full | Discovering Interdisciplinary Research Based on Neural Networks |
title_fullStr | Discovering Interdisciplinary Research Based on Neural Networks |
title_full_unstemmed | Discovering Interdisciplinary Research Based on Neural Networks |
title_short | Discovering Interdisciplinary Research Based on Neural Networks |
title_sort | discovering interdisciplinary research based on neural networks |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9203848/ https://www.ncbi.nlm.nih.gov/pubmed/35721858 http://dx.doi.org/10.3389/fbioe.2022.908733 |
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