Technological and computational advances driving high-throughput oncology.

Kolmar, Leonie; Autour, Alexis; Ma, Xiaoli; Vergier, Blandine; Eduati, Federica; Merten, Christoph A

Kolmar, Leonie; Autour, Alexis; Ma, Xiaoli; Vergier, Blandine; Eduati, Federica; Merten, Christoph A.

Afiliação

Kolmar L; Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Autour A; Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Ma X; Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Vergier B; Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Eduati F; Department of Biomedical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands; Institute for Complex Molecular Systems, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands. Electronic address: f.eduati@tue.nl.
Merten CA; Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. Electronic address: christoph.merten@epfl.ch.

Trends Cell Biol ; 32(11): 947-961, 2022 11.

Article em En | MEDLINE | ID: mdl-35577671

Assuntos

Neoplasias; Biologia Computacional/métodos; Humanos; Neoplasias/genética; Tecnologia

Palavras-chave

computational tools; microfluidics; personalized oncology; sequencing; single-cell multi-omics

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Limite: Humans Idioma: En Revista: Trends Cell Biol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Suíça

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