The KEGG database.

By Minoru Kanehisa. Novartis Foundation symposium, 2002

1,395 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/aec58dd1363d679e9e1d7917d74c0bcde504b65f

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In Molecular Biology

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Citations in the corpus

• Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, methodology): “This type of model construction has been benefiting greatly from databases like KEGG [87,88], MetaCyc [89], and Brenda [90], which contain large collections of pathway topologies and kinetic parameters retrieved from literature sources.”
• Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community (2019, methodology): “- Rapidly design new pathway maps based on user data and genome-scale models - Visualize data related to genes or proteins on the associated reactions and pathways - Identify trends in common genomic data types Open (MIT license) [116] Gaggle Variety of omics platformbioinformatics solutions Unspeci”
• Advancing translational research with the Semantic Web (2007, methodology): “Among these data sets are the OBO ontologies [69], Reac-tome [70], KEGG [71], NCI Metathesaurus [72], and Uni-Prot [73].”
• Co-occurrence of resistance genes to antibiotics, biocides and metals reveals novel insights into their co-selection potential (2015, background): “KEGG [18] databases (Additional file 2: Table S1).”
• The M5nr: a novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools (2012, methodology): “In addition to storing identifiers, functional annotations and taxonomic information (e.g NCBI taxonomy IDs), we also store mappings to functional hierarchies including GO [16], KEGG orthologs and pathways [17], SEED Subsystems [9], eggNOGs and NCBI COGs.”
• g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments (2007, background): “…of such a list may, for instance, be a cluster of co-expressed genes from a microarray experiment (1,11), predicted network of interacting proteins (3), direct targets of transcription factors (TFs) from genome-wide localisation studies (4), or genes with predicted TF-binding sites in their…”
• The Human Urine Metabolome (2013, background): “Each MetaboCard entry contains up to 120 data fields many of which are hyperlinked to other databases (KEGG [45], PubChem [46], MetaCyc [47], ChEBI [48], PDB [49], UniProt [50], and GenBank [51] as well as to GeneCard IDs [52], GeneAtlas IDs [53] and HGNC IDs [54] for each of the corresponding enzym”
• The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo (2006, background): “…following: coherence in expression data across subsets of experimental conditions; co-occurrence of putative cis -acting regulatory motifs in the regulatory regions of bicluster members; and the presence of highly connected subgraphs in metabolic [23] and functional association networks [24-26].”
• PathwayVoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (2005, background): “The Kyoto Encyclopedia of Genes and Genomes (KEGG) represents an ambitious and successful attempt to assign known enzymes into known biochemical pathways and is updated on a regular basis [1-3].”