Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data.

Shi, Mingguang; Li, Xuefeng; Li, Mingna; Si, Yichong

Shi, Mingguang; Li, Xuefeng; Li, Mingna; Si, Yichong.

Afiliação

Shi M; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China.
Li X; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China.
Li M; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China.
Si Y; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, Anhui 230009, China.

Brief Bioinform ; 24(6)2023 09 22.

Article em En | MEDLINE | ID: mdl-37756592

Assuntos

Carcinoma de Células Renais; Glioma; Neoplasias Renais; MicroRNAs; Humanos; Prognóstico

Palavras-chave

generative adversarial networks; multimodal data; prognostic outcome prediction; sequential channel-spatial attention modules

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Carcinoma de Células Renais / MicroRNAs / Glioma / Neoplasias Renais Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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