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Cancer Research Trend Analysis Based on Fusion Feature Representation
Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word repre...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001649/ https://www.ncbi.nlm.nih.gov/pubmed/33809188 http://dx.doi.org/10.3390/e23030338 |
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author | Wu, Jingqiao Feng, Xiaoyue Guan, Renchu Liang, Yanchun |
author_facet | Wu, Jingqiao Feng, Xiaoyue Guan, Renchu Liang, Yanchun |
author_sort | Wu, Jingqiao |
collection | PubMed |
description | Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research. |
format | Online Article Text |
id | pubmed-8001649 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80016492021-03-28 Cancer Research Trend Analysis Based on Fusion Feature Representation Wu, Jingqiao Feng, Xiaoyue Guan, Renchu Liang, Yanchun Entropy (Basel) Article Machine learning models can automatically discover biomedical research trends and promote the dissemination of information and knowledge. Text feature representation is a critical and challenging task in natural language processing. Most methods of text feature representation are based on word representation. A good representation can capture semantic and structural information. In this paper, two fusion algorithms are proposed, namely, the Tr-W2v and Ti-W2v algorithms. They are based on the classical text feature representation model and consider the importance of words. The results show that the effectiveness of the two fusion text representation models is better than the classical text representation model, and the results based on the Tr-W2v algorithm are the best. Furthermore, based on the Tr-W2v algorithm, trend analyses of cancer research are conducted, including correlation analysis, keyword trend analysis, and improved keyword trend analysis. The discovery of the research trends and the evolution of hotspots for cancers can help doctors and biological researchers collect information and provide guidance for further research. MDPI 2021-03-12 /pmc/articles/PMC8001649/ /pubmed/33809188 http://dx.doi.org/10.3390/e23030338 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Wu, Jingqiao Feng, Xiaoyue Guan, Renchu Liang, Yanchun Cancer Research Trend Analysis Based on Fusion Feature Representation |
title | Cancer Research Trend Analysis Based on Fusion Feature Representation |
title_full | Cancer Research Trend Analysis Based on Fusion Feature Representation |
title_fullStr | Cancer Research Trend Analysis Based on Fusion Feature Representation |
title_full_unstemmed | Cancer Research Trend Analysis Based on Fusion Feature Representation |
title_short | Cancer Research Trend Analysis Based on Fusion Feature Representation |
title_sort | cancer research trend analysis based on fusion feature representation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001649/ https://www.ncbi.nlm.nih.gov/pubmed/33809188 http://dx.doi.org/10.3390/e23030338 |
work_keys_str_mv | AT wujingqiao cancerresearchtrendanalysisbasedonfusionfeaturerepresentation AT fengxiaoyue cancerresearchtrendanalysisbasedonfusionfeaturerepresentation AT guanrenchu cancerresearchtrendanalysisbasedonfusionfeaturerepresentation AT liangyanchun cancerresearchtrendanalysisbasedonfusionfeaturerepresentation |