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

Wang, Kangtao; Zheng, Chanjuan; Xue, Lian; Deng, Dexin; Zeng, Liang; Li, Ming; Deng, Xiyun.

Affiliation

Wang K; Department of General Surgery, The Xiangya Hospital, Central South University, Changsha, Hunan, China.
Zheng C; Key Laboratory of Model Animals and Stem Cell Biology in Hunan, Department of Pathophysiology, School of Medicine, Hunan Normal University, Changsha, Hunan, China.
Xue L; Key Laboratory of Model Animals and Stem Cell Biology in Hunan, Department of Pathophysiology, School of Medicine, Hunan Normal University, Changsha, Hunan, China.
Deng D; Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
Zeng L; Department of Pathology, Guangzhou Women and Children's Medical Center, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China.
Li M; Department of Immunology, College of Basic Medical Sciences, Central South University, Changsha, Hunan, China.
Deng X; Key Laboratory of Model Animals and Stem Cell Biology in Hunan, Department of Pathophysiology, School of Medicine, Hunan Normal University, Changsha, Hunan, China.

Front Med (Lausanne) ; 10: 999312, 2023.

Article de En | MEDLINE | ID: mdl-36844225

Mots clés

Latent Dirichlet Allocation; Nanoparticle research; bibliometric analysis; machine learning; triple-negative breast cancer

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Front Med (Lausanne) Année: 2023 Type de document: Article Pays d'affiliation: Chine Pays de publication: Suisse

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