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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.
Rozova, Vlada S; Anwer, Ayad G; Guller, Anna E; Es, Hamidreza Aboulkheyr; Khabir, Zahra; Sokolova, Anastasiya I; Gavrilov, Maxim U; Goldys, Ewa M; Warkiani, Majid Ebrahimi; Thiery, Jean Paul; Zvyagin, Andrei V.
Afiliação
  • Rozova VS; ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Anwer AG; Institute for Biology and Biomedicine, Lobachevsky State University, Nizhny Novgorod, Russia.
  • Guller AE; ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Es HA; Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.
  • Khabir Z; ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Sokolova AI; Graduate School of Biomedical Engineering, University of New South Wales, Sydney, Australia.
  • Gavrilov MU; Institute for Regenerative Medicine, Sechenov University, Moscow, Russia.
  • Goldys EM; School of Biomedical Engineering, University of Technology Sydney, Sydney, Australia.
  • Warkiani ME; ARC Centre of Excellence for Nanoscale Biophotonics, Macquarie University, Sydney, Australia.
  • Thiery JP; Centre of Biomedical Engineering, Sechenov University, Moscow, Russia.
  • Zvyagin AV; Laboratory of Medical Nanotechnologies, Federal Biomedical Agency, Moscow, Russia.
PLoS Comput Biol ; 17(7): e1009193, 2021 07.
Article em En | MEDLINE | ID: mdl-34297718
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transição Epitelial-Mesenquimal / Neoplasias de Mama Triplo Negativas / Aprendizado de Máquina / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália
Texto completo
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transição Epitelial-Mesenquimal / Neoplasias de Mama Triplo Negativas / Aprendizado de Máquina / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Austrália