SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso.

Huo, Yanhao; Xin, Lihui; Kang, Chuanze; Wang, Minghui; Ma, Qin; Yu, Bin

Huo, Yanhao; Xin, Lihui; Kang, Chuanze; Wang, Minghui; Ma, Qin; Yu, Bin.

Afiliação

Huo Y; College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China.
Xin L; School of Science, Dalian University of Technology, Panjin 124221, China.
Kang C; College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China.
Wang M; College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China.
Ma Q; Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio 43210, USA.
Yu B; College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China; School of Life Sciences, University of Science and Technology

J Theor Biol ; 486: 110098, 2020 02 07.

Article em En | MEDLINE | ID: mdl-31786183

Assuntos

Neoplasias; Máquina de Vetores de Suporte; Algoritmos; Perfilação da Expressão Gênica; Humanos; Análise em Microsséries; Neoplasias/genética; Software

Palavras-chave

Feature selection; Gene expression data; Sparse group Lasso; Support vector machine; Tumor classification

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Máquina de Vetores de Suporte / Neoplasias Limite: Humans Idioma: En Revista: J Theor Biol Ano de publicação: 2020 Tipo de documento: Article País de afiliação: China País de publicação: Reino Unido

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