Machine learning predicts cancer subtypes and progression from blood immune signatures.

Simon Davis, David A; Mun, Sahngeun; Smith, Julianne M; Hammill, Dillon; Garrett, Jessica; Gosling, Katharine; Price, Jason; Elsaleh, Hany; Syed, Farhan M; Atmosukarto, Ines I; Quah, Benjamin J C

Simon Davis, David A; Mun, Sahngeun; Smith, Julianne M; Hammill, Dillon; Garrett, Jessica; Gosling, Katharine; Price, Jason; Elsaleh, Hany; Syed, Farhan M; Atmosukarto, Ines I; Quah, Benjamin J C.

Afiliação

Simon Davis DA; Irradiation Immunity Interaction Lab, Canberra, ACT, Australia.
Mun S; Irradiation Immunity Interaction Lab, Canberra, ACT, Australia.
Smith JM; Irradiation Immunity Interaction Lab, Canberra, ACT, Australia.
Hammill D; Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Garrett J; Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Gosling K; Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Price J; Division of Genome Sciences & Cancer, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.
Elsaleh H; Radiation Oncology Department, The Alfred, Melbourne, VIC, Australia.
Syed FM; Irradiation Immunity Interaction Lab, Canberra, ACT, Australia.
Atmosukarto II; Radiation Oncology Department, Canberra Hospital, Canberra Health Services, Canberra, ACT, Australia.
Quah BJC; Irradiation Immunity Interaction Lab, Canberra, ACT, Australia.

PLoS One ; 17(2): e0264631, 2022.

Article em En | MEDLINE | ID: mdl-35226704

Assuntos

Antígeno B7-H1

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Antígeno B7-H1 Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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