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 128 articles )

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  1. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  2. Barish, Robert D.; Suyama, Akira: Counting substrate cycles in topologically restricted metabolic networks (2017)
  3. Miok, Viktorian; Wilting, Saskia M.; van Wieringen, Wessel N.: Ridge estimation of the VAR(1) model and its time series chain graph from multivariate time-course omics data (2017)
  4. Zimmer, Björn; Kerren, Andreas: OnGraX: a web-based system for the collaborative visual analysis of graphs (2017)
  5. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  6. Chaley, Maria; Kutyrkin, Vladimir: Stochastic model of homogeneous coding and latent periodicity in DNA sequences (2016)
  7. Indhumathy, M.; Arumugam, S.; Baths, Veeky; Singh, Tarkeshwar: Graph theoretic concepts in the study of biological networks (2016)
  8. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  9. Nakatsuji, Makoto; Toda, Hiroyuki; Sawada, Hiroshi; Zheng, Jin Guang; Hendler, James A.: Semantic sensitive tensor factorization (2016)
  10. Pesch, Robert: Cross-species network and transcript transfer (2016)
  11. Wu, Xuesong; Tang, Haoran; Guan, Aoran; Sun, Feng; Wang, Hui; Shu, Jie: Finding gastric cancer related genes and clinical biomarkers for detection based on gene-gene interaction network (2016)
  12. Akutsu, Tatsuya; Tamura, Takeyuki; Melkman, Avraham A.; Takasu, Atsuhiro: On the complexity of finding a largest common subtree of bounded degree (2015)
  13. Chekouo, Thierry; Murua, Alejandro; Raffelsberger, Wolfgang: The Gibbs-plaid biclustering model (2015)
  14. Chekouo, Thierry; Stingo, Francesco C.; Doecke, James D.; Do, Kim-Anh: miRNA-target gene regulatory networks: a Bayesian integrative approach to biomarker selection with application to kidney cancer (2015)
  15. Djordjilović, Vera; Chiogna, Monica; Massa, M.Sofia; Romualdi, Chiara: Graphical modeling for gene set analysis: a critical appraisal (2015)
  16. Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence; Rau, Andrea: A model selection criterion for model-based clustering of annotated gene expression data (2015)
  17. Heinle, Albert; Levandovskyy, Viktor: The SDEval benchmarking toolkit (2015)
  18. Hinow, Peter; Rietman, Edward A.; Omar, Sara Ibrahim; Tuszyński, Jack A.: Algebraic and topological indices of molecular pathway networks in human cancers (2015)
  19. Ito, Sohei; Ichinose, Takuma; Shimakawa, Masaya; Izumi, Naoko; Hagihara, Shigeki; Yonezaki, Naoki: Qualitative analysis of gene regulatory networks by temporal logic (2015)
  20. Lee, Juhee; Müller, Peter; Gulukota, Kamalakar; Ji, Yuan: A Bayesian feature allocation model for tumor heterogeneity (2015)

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