DualGCN: a dual graph convolutional network model to predict cancer drug response.

Ma, Tianxing; Liu, Qiao; Li, Haochen; Zhou, Mu; Jiang, Rui; Zhang, Xuegong

Ma, Tianxing; Liu, Qiao; Li, Haochen; Zhou, Mu; Jiang, Rui; Zhang, Xuegong.

Afiliação

Ma T; MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST and Department of Automation, Tsinghua University, Beijing, 100084, China.
Liu Q; Department of Statistics, Stanford University, Stanford, CA, 94305, USA.
Li H; School of Medicine, Center for Synthetic and Systems Biology, Tsinghua University, Beijing, 100084, China.
Zhou M; SenseBrain Research, San Jose, CA, 95131, USA.
Jiang R; MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST and Department of Automation, Tsinghua University, Beijing, 100084, China.
Zhang X; MOE Key Laboratory of Bioinformatics, Bioinformatics Division, BNRIST and Department of Automation, Tsinghua University, Beijing, 100084, China. zhangxg@tsinghua.edu.cn.

BMC Bioinformatics ; 23(Suppl 4): 129, 2022 Apr 15.

Article em En | MEDLINE | ID: mdl-35428192

Assuntos

Antineoplásicos; Neoplasias; Antineoplásicos/farmacologia; Antineoplásicos/uso terapêutico; Genômica; Humanos; Neoplasias/tratamento farmacológico; Neoplasias/genética; Redes Neurais de Computação; Microambiente Tumoral

Palavras-chave

Cancer drug response; Graph convolutional networks; Proteinprotein interactions; Tumor heterogeneity

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

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

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google