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The evolution of knowledge on genes associated with human diseases.
Lüscher-Dias, Thomaz; Siqueira Dalmolin, Rodrigo Juliani; de Paiva Amaral, Paulo; Alves, Tiago Lubiana; Schuch, Viviane; Franco, Glória Regina; Nakaya, Helder I.
Afiliação
  • Lüscher-Dias T; Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
  • Siqueira Dalmolin RJ; Bioinformatics Multidisciplinary Environment-BioME, IMD, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
  • de Paiva Amaral P; Department of Biochemistry, CB, Federal University of Rio Grande do Norte, Natal, RN, Brazil.
  • Alves TL; Instituto de Ensino e Pesquisa, Insper, São Paulo, Brazil.
  • Schuch V; Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil.
  • Franco GR; Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of São Paulo, São Paulo, Brazil.
  • Nakaya HI; Department of Biochemistry and Immunology, Institute of Biological Sciences, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
iScience ; 25(1): 103610, 2022 Jan 21.
Article em En | MEDLINE | ID: mdl-35005554
Thousands of biomedical scientific articles, including those describing genes associated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automatically detect these associations. In this study, we used a cognitive computing text-mining application to construct a knowledge network comprising 3,723 genes and 99 diseases. We then tracked the yearly changes on these networks to analyze how our knowledge has evolved in the past 30 years. Our systems approach helped to unravel the molecular bases of diseases and detect shared mechanisms between clinically distinct diseases. It also revealed that multi-purpose therapeutic drugs target genes that are commonly associated with several psychiatric, inflammatory, or infectious disorders. By navigating this knowledge tsunami, we were able to extract relevant biological information and insights about human diseases.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Revista: IScience Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Risk_factors_studies Idioma: En Revista: IScience Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Brasil País de publicação: Estados Unidos