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Gene Saturation: An Approach to Assess Exploration Stage of Gene Interaction Networks.
Yin, Ziqiao; Guo, Binghui; Mi, Zhilong; Li, Jiahui; Zheng, Zhiming.
Afiliación
  • Yin Z; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China.
  • Guo B; Shenyuan Honors College and School of Mathematics and Systems Science, Beihang University, Beijing, 100191, China.
  • Mi Z; LMIB and Peng Cheng Laboratory, Shenzhen, 518055, Guangdong, China.
  • Li J; Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China. guobinghui@buaa.edu.cn.
  • Zheng Z; Shenyuan Honors College and School of Mathematics and Systems Science, Beihang University, Beijing, 100191, China. guobinghui@buaa.edu.cn.
Sci Rep ; 9(1): 5017, 2019 03 21.
Article en En | MEDLINE | ID: mdl-30899072
The gene interaction network is one of the most important biological networks and has been studied by many researchers. The gene interaction network provides information about whether the genes in the network can cause or heal diseases. As gene-gene interaction relations are constantly explored, gene interaction networks are evolving. To describe how much a gene has been studied, an approach based on a logistic model for each gene called gene saturation has been proposed, which in most cases, satisfies non-decreasing, correlation and robustness principles. The average saturation of a group of genes can be used to assess the network constructed by these genes. Saturation reflects the distance between known gene interaction networks and the real gene interaction network in a cell. Furthermore, the saturation values of 546 disease gene networks that belong to 15 categories of diseases have been calculated. The disease gene networks' saturation for cancer is significantly higher than that of all other diseases, which means that the disease gene networks' structure for cancer has been more deeply studied than other disease. Gene saturation provides guidance for selecting an experimental subject gene, which may have a large number of unknown interactions.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / Epistasis Genética / Redes Reguladoras de Genes Límite: Humans Idioma: En Revista: Sci Rep Año: 2019 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Biología Computacional / Epistasis Genética / Redes Reguladoras de Genes Límite: Humans Idioma: En Revista: Sci Rep Año: 2019 Tipo del documento: Article País de afiliación: China Pais de publicación: Reino Unido