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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...

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Detalles Bibliográficos
Autores principales: Wu, Jingqiao, Feng, Xiaoyue, Guan, Renchu, Liang, Yanchun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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.
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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
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