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Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images.
Partin, Alexander; Brettin, Thomas; Zhu, Yitan; Dolezal, James M; Kochanny, Sara; Pearson, Alexander T; Shukla, Maulik; Evrard, Yvonne A; Doroshow, James H; Stevens, Rick L.
Afiliação
  • Partin A; Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States.
  • Brettin T; Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States.
  • Zhu Y; Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States.
  • Dolezal JM; Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, United States.
  • Kochanny S; Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, United States.
  • Pearson AT; Section of Hematology/Oncology, Department of Medicine, University of Chicago Medical Center, Chicago, IL, United States.
  • Shukla M; Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States.
  • Evrard YA; Frederick National Laboratory for Cancer Research, Leidos Biomedical Research, Inc., Frederick, MD, United States.
  • Doroshow JH; Division of Cancer Therapeutics and Diagnosis, National Cancer Institute, Bethesda, MD, United States.
  • Stevens RL; Division of Data Science and Learning, Argonne National Laboratory, Lemont, IL, United States.
Front Med (Lausanne) ; 10: 1058919, 2023.
Article em En | MEDLINE | ID: mdl-36960342
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Med (Lausanne) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Med (Lausanne) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos