Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.

Nicholls, Hannah L; John, Christopher R; Watson, David S; Munroe, Patricia B; Barnes, Michael R; Cabrera, Claudia P

Nicholls, Hannah L; John, Christopher R; Watson, David S; Munroe, Patricia B; Barnes, Michael R; Cabrera, Claudia P.

Afiliação

Nicholls HL; Clinical Pharmacology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
John CR; Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
Watson DS; Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
Munroe PB; Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
Barnes MR; Centre for Translational Bioinformatics, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom.
Cabrera CP; Oxford Internet Institute, University of Oxford, Oxford, United Kingdom.

Front Genet ; 11: 350, 2020.

Article em En | MEDLINE | ID: mdl-32351543

Palavras-chave

artificial intelligence; candidate gene; clinical translation; data science; deep learning; genome-wide association study; genomics; machine learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Front Genet Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Reino Unido

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