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 138 articles )

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  1. Álvarez-Miranda, Eduardo; Farhan, Hesso; Luipersbeck, Martin; Sinnl, Markus: A bi-objective network design approach for discovering functional modules linking Golgi apparatus fragmentation and neuronal death (2017)
  2. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  3. Barish, Robert D.; Suyama, Akira: Counting substrate cycles in topologically restricted metabolic networks (2017)
  4. Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin: Pathway-based kernel boosting for the analysis of genome-wide association studies (2017)
  5. Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)
  6. Li, Quefeng; Yu, Menggang; Wang, Sijian: A statistical framework for pathway and gene identification from integrative analysis (2017)
  7. 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)
  8. Shafaghati, Leila; Razaghi-Moghadam, Zahra; Mohammadnejad, Javad: A systems biology approach to understanding alcoholic liver disease molecular mechanism: the development of static and dynamic models (2017)
  9. Wei, Pi-Jing; Zhang, Di; Li, Hai-Tao; Xia, Junfeng; Zheng, Chun-Hou: DriverFinder: a gene length-based network method to identify cancer driver genes (2017)
  10. Yan, Cheng; Wang, Jianxin; Lan, Wei; Wu, Fang-Xiang; Pan, Yi: SDTRLS: predicting drug-target interactions for complex diseases based on chemical substructures (2017)
  11. Zimmer, Björn; Kerren, Andreas: OnGraX: a web-based system for the collaborative visual analysis of graphs (2017)
  12. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  13. Chaley, Maria; Kutyrkin, Vladimir: Stochastic model of homogeneous coding and latent periodicity in DNA sequences (2016)
  14. Děd, T.; Šafránek, D.; Troják, M.; Klement, M.; Šalagovič, J.; Brim, L.: Formal biochemical space with semantics in kappa and BNGL (2016)
  15. Indhumathy, M.; Arumugam, S.; Baths, Veeky; Singh, Tarkeshwar: Graph theoretic concepts in the study of biological networks (2016)
  16. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  17. Nakatsuji, Makoto; Toda, Hiroyuki; Sawada, Hiroshi; Zheng, Jin Guang; Hendler, James A.: Semantic sensitive tensor factorization (2016)
  18. Pesch, Robert: Cross-species network and transcript transfer (2016)
  19. 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)
  20. Akutsu, Tatsuya; Tamura, Takeyuki; Melkman, Avraham A.; Takasu, Atsuhiro: On the complexity of finding a largest common subtree of bounded degree (2015)

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