Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks.

Weitz, Philippe; Wang, Yinxi; Kartasalo, Kimmo; Egevad, Lars; Lindberg, Johan; Grönberg, Henrik; Eklund, Martin; Rantalainen, Mattias

Weitz, Philippe; Wang, Yinxi; Kartasalo, Kimmo; Egevad, Lars; Lindberg, Johan; Grönberg, Henrik; Eklund, Martin; Rantalainen, Mattias.

Afiliação

Weitz P; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.
Wang Y; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.
Kartasalo K; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.
Egevad L; Faculty of Medicine and Health Technology, Tampere University, 33100 Tampere, Finland.
Lindberg J; Department of Oncology and Pathology, Karolinska Institutet, 17177 Stockholm, Sweden.
Grönberg H; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.
Eklund M; Science for Life Laboratory, 17177 Stockholm, Sweden.
Rantalainen M; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177 Stockholm, Sweden.

Bioinformatics ; 38(13): 3462-3469, 2022 06 27.

Article em En | MEDLINE | ID: mdl-35595235

Assuntos

Neoplasias da Próstata; Transcriptoma; Humanos; Masculino; Redes Neurais de Computação; Neoplasias da Próstata/genética; Neoplasias da Próstata/patologia; Proteínas; Amarelo de Eosina-(YS)

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias da Próstata / Transcriptoma Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans / Male Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Suécia

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