A supervised machine learning approach identifies gene-regulating factor-mediated competing endogenous RNA networks in hormone-dependent cancers.

Jayarathna, Dulari K; Rentería, Miguel E; Batra, Jyotsna; Gandhi, Neha S

Jayarathna, Dulari K; Rentería, Miguel E; Batra, Jyotsna; Gandhi, Neha S.

Afiliação

Jayarathna DK; Centre for Genomics and Personalized Health, School of Chemistry and Physics, Queensland University of Technology, Brisbane, QLD, Australia.
Rentería ME; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Batra J; Department of Genetics and Computational Biology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
Gandhi NS; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.

J Cell Biochem ; 123(8): 1394-1408, 2022 08.

Article em En | MEDLINE | ID: mdl-35757968

Assuntos

Neoplasias Colorretais; MicroRNAs; RNA Longo não Codificante; Neoplasias Colorretais/genética; Feminino; Regulação Neoplásica da Expressão Gênica; Redes Reguladoras de Genes; Hormônios; Humanos; Masculino; MicroRNAs/genética; MicroRNAs/metabolismo; RNA Longo não Codificante/genética; Aprendizado de Máquina Supervisionado

Palavras-chave

DNA methylation; LASSO regression; competing endogenous RNA; copy number alteration; machine learning; sparse correlation; transcription factors

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Limite: Female / Humans / Male Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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