The KEGG databases at GenomeNet

By Minoru Kanehisa, Susumu Goto, Shuichi Kawashima, Akihiro Nakaya. Nucleic Acids Res., 2002

1,523 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c92c2c542b6189f1e675223effdd71addab888d0

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Citations in the corpus

Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs (2002, background): “In comparison, the human enzyme class in the KEGG metabolic pathway databas”
Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms (2003, methodology): “The KEGG databases are continually updated by incorporating the most up-to-date gene annotation information from various databases (Kanehisa et al., 2002).”
A module map showing conditional activity of expression modules in cancer (2004, background): “and genes belonging to the same functional category or pathwa”
RTCGD: retroviral tagged cancer gene database (2004, neutral): “Links are also provided to the KEGG (19) and Gene Ontology (20) databases in order to help facilitate annotation.”
High-throughput generation, optimization and analysis of genome-scale metabolic models (2010, methodology): “This database combines all the biochemistry contained in the KEG”
Metabolomics by numbers: acquiring and understanding global metabolite data. (2004, methodology): “Supervised machine learning algorithms [ 48 ] are very powerful methods that seek to transform the multivariate data from metabolite profiles into something of biological interest under the guidance of a ‘teacher’ (Figure 2). The basic idea behind supervised learning is that there are some patterns ”
Systematic interpretation of genetic interactions using protein networks (2005, neutral): “Enzymatic reactions linked by common metabolites were obtained from KEG”
ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks (2009, background): “As most of these tools mainly present their results as long lists or complex Cytoscape ( hierarchical trees, we aimed to develop ClueGO a Shannon et al. , 2003) plug-in to facilitate the biological interpretation and to visualize functionally grouped terms in the form of networks and charts.”
A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans (2008, methodology): “A second set of functional annotation was derived from KEGG databas”
Clustering of database sequences for fast homology search using upper bounds on alignment score. (2004, methodology): “1 Data Set To estimate the practical efficiency of our clustering method, we applied it to the KEGG/GENES database using the KEGG/SSDB [10, 11].”