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Disease classification: from phenotypic similarity to integrative genomics and beyond.
Dozmorov, Mikhail G.
Afiliação
  • Dozmorov MG; Department of Biostatistics, Virginia Commonwealth University, 830 East Main Street, Richmond, VA, USA.
Brief Bioinform ; 20(5): 1769-1780, 2019 09 27.
Article em En | MEDLINE | ID: mdl-29939197
A fundamental challenge of modern biomedical research is understanding how diseases that are similar on the phenotypic level are similar on the molecular level. Integration of various genomic data sets with the traditionally used phenotypic disease similarity revealed novel genetic and molecular mechanisms and blurred the distinction between monogenic (Mendelian) and complex diseases. Network-based medicine has emerged as a complementary approach for identifying disease-causing genes, genetic mediators, disruptions in the underlying cellular functions and for drug repositioning. The recent development of machine and deep learning methods allow for leveraging real-life information about diseases to refine genetic and phenotypic disease relationships. This review describes the historical development and recent methodological advancements for studying disease classification (nosology).
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fenótipo / Genômica / Doenças Genéticas Inatas Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Fenótipo / Genômica / Doenças Genéticas Inatas Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Brief Bioinform Assunto da revista: BIOLOGIA / INFORMATICA MEDICA Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Estados Unidos