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 108 articles )

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  1. Zimmer, Björn; Kerren, Andreas: OnGraX: a web-based system for the collaborative visual analysis of graphs (2017)
  2. Adhikari, Prem Raj; Vavpetič, Anže; Kralj, Jan; Lavrač, Nada; Hollmén, Jaakko: Explaining mixture models through semantic pattern mining and banded matrix visualization (2016)
  3. Chaley, Maria; Kutyrkin, Vladimir: Stochastic model of homogeneous coding and latent periodicity in DNA sequences (2016)
  4. Mathé, Ewy (ed.); Davis, Sean (ed.): Statistical genomics. Methods and protocols (2016)
  5. Nakatsuji, Makoto; Toda, Hiroyuki; Sawada, Hiroshi; Zheng, Jin Guang; Hendler, James A.: Semantic sensitive tensor factorization (2016)
  6. Pesch, Robert: Cross-species network and transcript transfer (2016)
  7. 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)
  8. Akutsu, Tatsuya; Tamura, Takeyuki; Melkman, Avraham A.; Takasu, Atsuhiro: On the complexity of finding a largest common subtree of bounded degree (2015)
  9. 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)
  10. Heinle, Albert; Levandovskyy, Viktor: The SDEval benchmarking toolkit (2015)
  11. Hinow, Peter; Rietman, Edward A.; Omar, Sara Ibrahim; Tuszyński, Jack A.: Algebraic and topological indices of molecular pathway networks in human cancers (2015)
  12. Ito, Sohei; Ichinose, Takuma; Shimakawa, Masaya; Izumi, Naoko; Hagihara, Shigeki; Yonezaki, Naoki: Qualitative analysis of gene regulatory networks by temporal logic (2015)
  13. Li, Limin; Aoki-Kinoshita, Kiyoko F.; Ching, Wai-Ki; Jiang, Hao: On using physico-chemical properties of amino acids in string kernels for protein classification via support vector machines (2015)
  14. Makhlouf, Amar; Bousbiat, Lilia: Periodic solutions of some polynomial differential systems in dimension 3 via averaging theory (2015)
  15. Oddsdóttir, Hildur Æsa; Hagrot, Erika; Chotteau, Véronique; Forsgren, Anders: On dynamically generating relevant elementary flux modes in a metabolic network using optimization (2015)
  16. Picardi, Ernesto (ed.): RNA bioinformatics (2015)
  17. Shin, Kilho: A theory of subtree matching and tree kernels based on the edit distance concept (2015)
  18. van Wieringen, Wessel N.; van der Vaart, Aad W.: Transcriptomic heterogeneity in cancer as a consequence of dysregulation of the gene-gene interaction network (2015)
  19. Videla, Santiago; Guziolowski, Carito; Eduati, Federica; Thiele, Sven; Gebser, Martin; Nicolas, Jacques; Saez-Rodriguez, Julio; Schaub, Torsten; Siegel, Anne: Learning Boolean logic models of signaling networks with ASP (2015)
  20. Yang, Lingjian; Ainali, Chrysanthi; Kittas, Aristotelis; Nestle, Frank O.; Papageorgiou, Lazaros G.; Tsoka, Sophia: Pathway-level disease data mining through hyper-box principles (2015)

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