An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.

Chen, Sizhen; Zang, Yiteng; Xu, Biyun; Lu, Beier; Ma, Rongji; Miao, Pengcheng; Chen, Bingwei

Chen, Sizhen; Zang, Yiteng; Xu, Biyun; Lu, Beier; Ma, Rongji; Miao, Pengcheng; Chen, Bingwei.

Afiliação

Chen S; Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Zang Y; Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Xu B; Department of Biostatistics, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China.
Lu B; Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Ma R; Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Miao P; Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.
Chen B; Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing 210009, China.

Comput Math Methods Med ; 2022: 5844846, 2022.

Article em En | MEDLINE | ID: mdl-36339684

Assuntos

Adenocarcinoma; Aprendizado Profundo; Neoplasias Gástricas; Humanos; Prognóstico; Adenocarcinoma/genética; Adenocarcinoma/patologia; Neoplasias Gástricas/diagnóstico; Neoplasias Gástricas/genética

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Gástricas / Adenocarcinoma / Aprendizado Profundo Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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