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A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years

BACKGROUND: Triple-negative breast cancer (TNBC) is proposed at the beginning of this century, which is still the most challenging breast cancer subtype due to its aggressive behavior, including early relapse, metastatic spread, and poor survival. This study uses machine learning methods to explore...

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Detalles Bibliográficos
Autores principales: Wang, Kangtao, Zheng, Chanjuan, Xue, Lian, Deng, Dexin, Zeng, Liang, Li, Ming, Deng, Xiyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945529/
https://www.ncbi.nlm.nih.gov/pubmed/36844225
http://dx.doi.org/10.3389/fmed.2023.999312
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author Wang, Kangtao
Zheng, Chanjuan
Xue, Lian
Deng, Dexin
Zeng, Liang
Li, Ming
Deng, Xiyun
author_facet Wang, Kangtao
Zheng, Chanjuan
Xue, Lian
Deng, Dexin
Zeng, Liang
Li, Ming
Deng, Xiyun
author_sort Wang, Kangtao
collection PubMed
description BACKGROUND: Triple-negative breast cancer (TNBC) is proposed at the beginning of this century, which is still the most challenging breast cancer subtype due to its aggressive behavior, including early relapse, metastatic spread, and poor survival. This study uses machine learning methods to explore the current research status and deficiencies from a macro perspective on TNBC publications. METHODS: PubMed publications under “triple-negative breast cancer” were searched and downloaded between January 2005 and 2022. R and Python extracted MeSH terms, geographic information, and other abstracts from metadata. The Latent Dirichlet Allocation (LDA) algorithm was applied to identify specific research topics. The Louvain algorithm established a topic network, identifying the topic’s relationship. RESULTS: A total of 16,826 publications were identified, with an average annual growth rate of 74.7%. Ninety-eight countries and regions in the world participated in TNBC research. Molecular pathogenesis and medication are most studied in TNBC research. The publications mainly focused on three aspects: Therapeutic target research, Prognostic research, and Mechanism research. The algorithm and citation suggested that TNBC research is based on technology that advances TNBC subtyping, new drug development, and clinical trials. CONCLUSION: This study quantitatively analyzes the current status of TNBC research from a macro perspective and will aid in redirecting basic and clinical research toward a better outcome for TNBC. Therapeutic target research and Nanoparticle research are the present research focus. There may be a lack of research on TNBC from a patient perspective, health economics, and end-of-life care perspectives. The research direction of TNBC may require the intervention of new technologies.
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spelling pubmed-99455292023-02-23 A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years Wang, Kangtao Zheng, Chanjuan Xue, Lian Deng, Dexin Zeng, Liang Li, Ming Deng, Xiyun Front Med (Lausanne) Medicine BACKGROUND: Triple-negative breast cancer (TNBC) is proposed at the beginning of this century, which is still the most challenging breast cancer subtype due to its aggressive behavior, including early relapse, metastatic spread, and poor survival. This study uses machine learning methods to explore the current research status and deficiencies from a macro perspective on TNBC publications. METHODS: PubMed publications under “triple-negative breast cancer” were searched and downloaded between January 2005 and 2022. R and Python extracted MeSH terms, geographic information, and other abstracts from metadata. The Latent Dirichlet Allocation (LDA) algorithm was applied to identify specific research topics. The Louvain algorithm established a topic network, identifying the topic’s relationship. RESULTS: A total of 16,826 publications were identified, with an average annual growth rate of 74.7%. Ninety-eight countries and regions in the world participated in TNBC research. Molecular pathogenesis and medication are most studied in TNBC research. The publications mainly focused on three aspects: Therapeutic target research, Prognostic research, and Mechanism research. The algorithm and citation suggested that TNBC research is based on technology that advances TNBC subtyping, new drug development, and clinical trials. CONCLUSION: This study quantitatively analyzes the current status of TNBC research from a macro perspective and will aid in redirecting basic and clinical research toward a better outcome for TNBC. Therapeutic target research and Nanoparticle research are the present research focus. There may be a lack of research on TNBC from a patient perspective, health economics, and end-of-life care perspectives. The research direction of TNBC may require the intervention of new technologies. Frontiers Media S.A. 2023-02-08 /pmc/articles/PMC9945529/ /pubmed/36844225 http://dx.doi.org/10.3389/fmed.2023.999312 Text en Copyright © 2023 Wang, Zheng, Xue, Deng, Zeng, Li and Deng. 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 Medicine
Wang, Kangtao
Zheng, Chanjuan
Xue, Lian
Deng, Dexin
Zeng, Liang
Li, Ming
Deng, Xiyun
A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years
title A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years
title_full A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years
title_fullStr A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years
title_full_unstemmed A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years
title_short A bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: Progress in the past 17 years
title_sort bibliometric analysis of 16,826 triple-negative breast cancer publications using multiple machine learning algorithms: progress in the past 17 years
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9945529/
https://www.ncbi.nlm.nih.gov/pubmed/36844225
http://dx.doi.org/10.3389/fmed.2023.999312
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