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iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes.
Khan, Atlas; Liu, Qian; Wang, Kai.
Afiliação
  • Khan A; Division of Nephrology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.
  • Liu Q; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA.
  • Wang K; Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA. wangk@email.chop.edu.
BMC Bioinformatics ; 19(Suppl 17): 501, 2018 Dec 28.
Article em En | MEDLINE | ID: mdl-30591030
BACKGROUND: A range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches. METHODS: Leveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. iMEGES takes as input genetic mutations and phenotypic information from a patient with mental disorders, and outputs the rank of whole genome susceptibility variants and the prioritized disease-specific genes for mental disorders by integrating contributions from coding and non-coding variants, structural variants (SVs), known brain expression quantitative trait loci (eQTLs), and epigenetic information from PsychENCODE. RESULTS: iMEGES was evaluated on multiple datasets of mental disorders, and it achieved improved performance than competing approaches when large training dataset is available. CONCLUSION: iMEGES can be used in population studies to help the prioritization of novel genes or variants that might be associated with the susceptibility to mental disorders, and also on individual patients to help the identification of genes or variants related to mental diseases.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Genoma Humano / Redes Neurais de Computação / Predisposição Genética para Doença / Aprendizado Profundo / Transtornos Mentais Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Genoma Humano / Redes Neurais de Computação / Predisposição Genética para Doença / Aprendizado Profundo / Transtornos Mentais Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: BMC Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2018 Tipo de documento: Article País de afiliação: Estados Unidos