KEGG: Kyoto Encyclopedia of Genes and Genomes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from genomic and molecular-level information. It is a computer representation of the biological system, consisting of molecular building blocks of genes and proteins (genomic information) and chemical substances (chemical information) that are integrated with the knowledge on molecular wiring diagrams of interaction, reaction and relation networks (systems information). It also contains disease and drug information (health information) as perturbations to the biological system.

References in zbMATH (referenced in 246 articles )

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  1. Peeters, Carel F. W.; van de Wiel, Mark A.; van Wieringen, Wessel N.: The spectral condition number plot for regularization parameter evaluation (2020)
  2. Raymond Tobler, Angad Johar, Christian Huber, Yassine Souilmi: PolyLinkR: A linkage-sensitive gene set enrichment R package (2020) arXiv
  3. Su, Yansen; Zhu, Huole; Zhang, Lei; Zhang, Xingyi: Identifying disease modules based on connectivity and semantic similarities (2020)
  4. van Wieringen, Wessel N.; Stam, Koen A.; Peeters, Carel F. W.; van de Wiel, Mark A.: Updating of the Gaussian graphical model through targeted penalized estimation (2020)
  5. Wang, Yuhao; Segarra, Santiago; Uhler, Caroline: High-dimensional joint estimation of multiple directed Gaussian graphical models (2020)
  6. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  7. de Campos, Luis M.; Cano, Andrés; Castellano, Javier G.; Moral, Serafín: Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions (2019)
  8. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  9. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  10. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  11. Nicholson, Daniel J.: Is the cell \textitreallya machine? (2019)
  12. Poterie, A.; Dupuy, J.-F.; Monbet, V.; Rouvière, L.: Classification tree algorithm for grouped variables (2019)
  13. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  14. Röhl, Annika; Bockmayr, Alexander: Finding MEMo: minimum sets of elementary flux modes (2019)
  15. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  16. Wang, Shulei; Yuan, Ming: Combined hypothesis testing on graphs with applications to gene set enrichment analysis (2019)
  17. Yu, Xinghao; Xiao, Lishun; Zeng, Ping; Huang, Shuiping: Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information (2019)
  18. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  19. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  20. Latif, Majid jun.; May, Elebeoba E.: A multiscale agent-based model for the investigation of E. coli K12 metabolic response during biofilm formation (2018)

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