KEGG

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 252 articles )

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  1. Li, Chunlin; Shen, Xiaotong; Pan, Wei: Likelihood ratio tests for a large directed acyclic graph (2020)
  2. Peeters, Carel F. W.; van de Wiel, Mark A.; van Wieringen, Wessel N.: The spectral condition number plot for regularization parameter evaluation (2020)
  3. Raymond Tobler, Angad Johar, Christian Huber, Yassine Souilmi: PolyLinkR: A linkage-sensitive gene set enrichment R package (2020) arXiv
  4. Su, Yansen; Zhu, Huole; Zhang, Lei; Zhang, Xingyi: Identifying disease modules based on connectivity and semantic similarities (2020)
  5. 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)
  6. Wang, Yuhao; Segarra, Santiago; Uhler, Caroline: High-dimensional joint estimation of multiple directed Gaussian graphical models (2020)
  7. Wu, Chong; Xu, Gongjun; Shen, Xiaotong; Pan, Wei: A regularization-based adaptive test for high-dimensional GLMs (2020)
  8. Bucur, Ioan Gabriel; Claassen, Tom; Heskes, Tom: Large-scale local causal inference of gene regulatory relationships (2019)
  9. 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)
  10. Gallicchio, Claudio; Micheli, Alessio: Deep reservoir neural networks for trees (2019)
  11. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  12. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  13. 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
  14. Nicholson, Daniel J.: Is the cell \textitreallya machine? (2019)
  15. Poterie, A.; Dupuy, J.-F.; Monbet, V.; Rouvière, L.: Classification tree algorithm for grouped variables (2019)
  16. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  17. Röhl, Annika; Bockmayr, Alexander: Finding MEMo: minimum sets of elementary flux modes (2019)
  18. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  19. Wang, Shulei; Yuan, Ming: Combined hypothesis testing on graphs with applications to gene set enrichment analysis (2019)
  20. 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)

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