PLUS: Predicting cancer metastasis potential based on positive and unlabeled learning.

Zhou, Junyi; Lu, Xiaoyu; Chang, Wennan; Wan, Changlin; Lu, Xiongbin; Zhang, Chi; Cao, Sha

Zhou, Junyi; Lu, Xiaoyu; Chang, Wennan; Wan, Changlin; Lu, Xiongbin; Zhang, Chi; Cao, Sha.

Afiliação

Zhou J; Amgen Inc., Thousand Oaks, California, United States of America.
Lu X; Department of Biostatistics and Health Data Science, Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
Chang W; Department of BioHealth, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana, United States of America.
Wan C; Department of Biostatistics and Health Data Science, Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
Lu X; Department of Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana, United States of America.
Zhang C; Department of Biostatistics and Health Data Science, Department of Medical and Molecular Genetics and Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, United States of America.
Cao S; Department of Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana, United States of America.

PLoS Comput Biol ; 18(3): e1009956, 2022 03.

Article em En | MEDLINE | ID: mdl-35349572

Assuntos

Algoritmos; Neoplasias; Humanos

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: PLoS Comput Biol Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

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