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Patient-specific multi-omics models and the application in personalized combination therapy.
John, August; Qin, Bo; Kalari, Krishna R; Wang, Liewei; Yu, Jia.
Afiliação
  • John A; Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA.
  • Qin B; Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.
  • Kalari KR; Gastroenterology Research Unit, Mayo Clinic, Rochester, MN 55905, USA.
  • Wang L; Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA.
  • Yu J; Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA.
Future Oncol ; 16(23): 1737-1750, 2020 Aug.
Article em En | MEDLINE | ID: mdl-32462937
The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Modelos Estatísticos / Biologia Computacional / Medicina de Precisão / Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Modelos Estatísticos / Biologia Computacional / Medicina de Precisão / Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article