Please use this identifier to cite or link to this item: https://repositorio.insper.edu.br/handle/11224/4885
Type: Artigo Científico
Title: The evolution of knowledge on genes associated with human diseases
Author: Lüscher-Dias, Thomaz
Dalmolin, Rodrigo Juliani Siqueira
Amaral, Paulo de Paiva Rosa
Alves, Tiago Lubiana
Schuch, Viviane
Franco, Glória Regina
Nakaya, Helder I.
Publication Date: 2022
Abstract: Thousands of biomedical scientific articles, including those describing genes asso ciated with human diseases, are published every week. Computational methods such as text mining and machine learning algorithms are now able to automati cally 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-pur pose 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.
Keywords (english terms): Não informado
Language: Inglês
CNPq Area: Ciências Biológicas
URI: https://doi.org/10.1016/j.isci.2021.103610
Copyright: O INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR
Notes: Texto completo
Appears in Collections:Coleção de Artigos Científicos

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