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.

Afiliación

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 en En | MEDLINE | ID: mdl-35757968

Asunto(s)

Neoplasias Colorrectales; MicroARNs; ARN Largo no Codificante; Neoplasias Colorrectales/genética; Femenino; Regulación Neoplásica de la Expresión Génica; Redes Reguladoras de Genes; Hormonas; Humanos; Masculino; MicroARNs/genética; MicroARNs/metabolismo; ARN Largo no Codificante/genética; Aprendizaje Automático Supervisado

Palabras clave

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

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Neoplasias Colorrectales / MicroARNs / ARN Largo no Codificante Tipo de estudio: Prognostic_studies Límite: Female / Humans / Male Idioma: En Revista: J Cell Biochem Año: 2022 Tipo del documento: Article País de afiliación: Australia

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