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.

Afiliación

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 en En | MEDLINE | ID: mdl-37756592

Asunto(s)

Carcinoma de Células Renales; Glioma; Neoplasias Renales; MicroARNs; Humanos; Pronóstico

Palabras clave

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

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Carcinoma de Células Renales / MicroARNs / Glioma / Neoplasias Renales Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2023 Tipo del documento: Article País de afiliación: China

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