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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues.
Lewin, Alex; Saadi, Habib; Peters, James E; Moreno-Moral, Aida; Lee, James C; Smith, Kenneth G C; Petretto, Enrico; Bottolo, Leonardo; Richardson, Sylvia.
Afiliação
  • Lewin A; Department of Mathematics, Brunel University London.
  • Saadi H; Department of Epidemiology and Biostatistics, Imperial College London, London.
  • Peters JE; Cambridge Institute for Medical Research, University of Cambridge, Cambridge, MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge.
  • Moreno-Moral A; MRC Clinical Sciences Centre, Imperial College London, London, UK.
  • Lee JC; Cambridge Institute for Medical Research, University of Cambridge, Cambridge.
  • Smith KG; Cambridge Institute for Medical Research, University of Cambridge, Cambridge.
  • Petretto E; MRC Clinical Sciences Centre, Imperial College London, London, UK, Duke-NUS Graduate Medical School, Singapore, Singapore.
  • Bottolo L; Department of Mathematics, Imperial College London, London, UK and Department of Medical Genetics, University of Cambridge.
  • Richardson S; MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge.
Bioinformatics ; 32(4): 523-32, 2016 Feb 15.
Article em En | MEDLINE | ID: mdl-26504141
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Software / Doenças Inflamatórias Intestinais / Regulação da Expressão Gênica / Teorema de Bayes / Locos de Características Quantitativas / Redes Reguladoras de Genes / Inflamação Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article
Texto completo
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Software / Doenças Inflamatórias Intestinais / Regulação da Expressão Gênica / Teorema de Bayes / Locos de Características Quantitativas / Redes Reguladoras de Genes / Inflamação Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Animals / Humans Idioma: En Ano de publicação: 2016 Tipo de documento: Article