KEGG for representation and analysis of molecular networks involving diseases and drugs
Open accessAbstract
Most human diseases are complex multi-factorial diseases resulting from the combination of various genetic and environmental factors. In the KEGG database resource (http://www.genome.jp/kegg/), diseases are viewed as perturbed states of the molecular system, and drugs as perturbants to the molecular system. Disease information is computerized in two forms: pathway maps and gene/molecule lists. The KEGG PATHWAY database contains pathway maps for the molecular systems in both normal and perturbed states. In the KEGG DISEASE database, each disease is represented by a list of known disease genes, any known environmental factors at the molecular level, diagnostic markers and therapeutic drugs, which may reflect the underlying molecular system. The KEGG DRUG database contains chemical structures and/or chemical components of all drugs in Japan, including crude drugs and TCM (Traditional Chinese Medicine) formulas, and drugs in the USA and Europe. This database also captures knowledge about two types of molecular networks: the interaction network with target molecules, metabolizing enzymes, other drugs, etc. and the chemical structure transformation network in the history of drug development. The new disease/drug information resource named KEGG MEDICUS can be used as a reference knowledge base for computational analysis of molecular networks, especially, by integrating large-scale experimental datasets.
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Multiple oncogenes drive the transcriptional reprogramming required for tumor cell proliferation, and many cancer driver genes also disrupt metabolism.
“Several oncogenes coordinate the transcriptional reprogramming that tumor cells need to thrive, with many cancer driver genes also perturbing metabolism135,136.”
From The metabolic landscape of RAS-driven cancers from biology to therapy (2021) · cites this paper as [136]
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KEGG for linking genomes to life and the environment
Kanehisa, M. I., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm882 · Cited by 6,828 · see citing works
Self-citationThe International HapMap Project
†The International HapMap Consortium, Genotyping centres: Baylor College of Medicine and ParAllele BioScience, Gibbs, R. A., Belmont, J. W., Hardenbol, P., Willis, T. D., Yu, F., Chinese HapMap Consortium, Yang, H., Ch'ang, L.-Y., et al. (2003) · Nature · doi:10.1038/nature02168 · Cited by 6,057 · see citing works
The Universal Protein Resource (UniProt)
Bairoch, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki070 · Cited by 3,774 · see citing works
Stratton, M. R., Campbell, P. J., & Futreal, P. A. (2009) · Nature · doi:10.1038/nature07943 · Cited by 3,458 · see citing works
From genomics to chemical genomics: new developments in KEGG
Kanehisa, M. I. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj102 · Cited by 3,126 · see citing works
Self-citationGenome-wide association studies for common diseases and complex traits
Hirschhorn, J. N., & Daly, M. J. (2005) · Nature Reviews Genetics · doi:10.1038/nrg1521 · Cited by 2,853 · see citing works
NCBI Reference Sequences: current status, policy and new initiatives
Pruitt, K. D., Tatusova, T., Klimke, W., & Maglott, D. R. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn721 · Cited by 736 · cited 1× · see citing works
“(1) Gene information for completely sequenced genomes is computationally generated from RefSeq (7) and other public resources, and stored in the KEGG GENES database.”
BackgroundMcKusick's Online Mendelian Inheritance in Man (OMIM(R))
Amberger, J., Bocchini, C. A., Scott, A. F., & Hamosh, A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn665 · Cited by 717 · see citing works
The Universal Protein Resource (UniProt) 2009
The UniProt Consortium (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn664 · Cited by 546 · see citing works
A database for post-genome analysis
Kanehisa, M. I. (1997) · Trends in Genetics · doi:10.1016/s0168-9525(97)01223-7 · Cited by 420 · see citing works
Self-citationThe repertoire of desaturases and elongases reveals fatty acid variations in 56 eukaryotic genomes
Hashimoto, K., Yoshizawa, A. C., Okuda, S., Kuma, K., Goto, S., & Kanehisa, M. I. (2007) · Journal of Lipid Research · doi:10.1194/jlr.m700377-jlr200 · Cited by 189 · see citing works
Self-citationOh, M., Yamada, T., Hattori, M., Goto, S., & Kanehisa, M. I. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700006f · Cited by 85 · see citing works
Self-citationYamanishi, Y., Hattori, M., Kotera, M., Goto, S., & Kanehisa, M. I. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp223 · Cited by 83 · see citing works
Self-citationLinking databases and organisms: GenomeNet resources in Japan
Kanehisa, M. I. (1997) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(97)01130-4 · Cited by 74 · see citing works
Self-citationHashimoto, K., Tokimatsu, T., Kawano, S., Yoshizawa, A. C., Okuda, S., Goto, S., & Kanehisa, M. I. (2009) · Carbohydrate Research · doi:10.1016/j.carres.2009.03.001 · Cited by 47 · see citing works
Self-citationBNDB – The Biochemical Network Database
Küntzer, J., Backes, C., Blum, T., et al. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-367 · Cited by 42 · see citing works
Extraction and Analysis of Chemical Modification Patterns in Drug Development
Shigemizu, D., Araki, M., Okuda, S., et al. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci8003804 · Cited by 13 · see citing works
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clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259 · see citing works
KEGG: new perspectives on genomes, pathways, diseases and drugs
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1092 · Cited by 8,865 · see citing works
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Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr988 · Cited by 4,848 · see citing works
Self-citationGillet, L. C., Navarro, P., Tate, S., Röst, H., Selevsek, N., Reiter, L., Bonner, R., & Aebersold, R. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o111.016717 · Cited by 2,877 · see citing works
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Morgan, X. C., Tickle, T. L., Sokol, H., Gevers, D., Devaney, K. L., Ward, D. V., Reyes, J. A., Shah, S. A., LeLeiko, N., Snapper, S. B., et al. (2012) · Genome Biology · doi:10.1186/gb-2012-13-9-r79 · Cited by 2,713 · see citing works
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Horai, H., Arita, M., Kanaya, S., Nihei, Y., Ikeda, T., Suwa, K., Ojima, Y., Tanaka, K., Tanaka, S., Aoshima, K., et al. (2010) · Journal of Mass Spectrometry · doi:10.1002/jms.1777 · Cited by 2,529 · see citing works
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425 · see citing works
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Huttlin, E. L., Jedrychowski, M. P., Elias, J. E., Goswami, T., Rad, R., Beausoleil, S. A., Villén, J., Haas, W., Sowa, M. E., & Gygi, S. P. (2010) · Cell · doi:10.1016/j.cell.2010.12.001 · Cited by 1,778 · see citing works
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Segata, N., Waldron, L., Ballarini, A., Narasimhan, V., Jousson, O., & Huttenhower, C. (2012) · Nature Methods · doi:10.1038/nmeth.2066 · Cited by 1,776 · see citing works
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Orth, J. D., Conrad, T. M., Na, J., Lerman, J. A., Nam, H., Feist, A. M., & Palsson, B. Ø. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.65 · Cited by 1,006 · see citing works
Segata, N., Haake, S. K., Mannon, P., Lemon, K. P., Waldron, L., Gevers, D., Huttenhower, C., & Izard, J. (2012) · Genome Biology · doi:10.1186/gb-2012-13-6-r42 · Cited by 1,076 · see citing works
Dubin, K., Callahan, M. K., Ren, B., Khanin, R., Viale, A., Ling, L., No, D., Gobourne, A., Littmann, E., Huttenhower, C., et al. (2016) · Nature Communications · doi:10.1038/ncomms10391 · Cited by 987 · see citing works
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BRENDA, the enzyme information system in 2011
Scheer, M., Grote, A., Chang, A., Schomburg, I., Munaretto, C., Rother, M., Sohngen, C., Stelzer, M., Thiele, J., & Schomburg, D. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1089 · Cited by 409 · see citing works
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Schwimmer, J. B., Johnson, J. S., Angeles, J. E., Behling, C., Belt, P. H., Borecki, I., Bross, C., Durelle, J., Goyal, N. P., Hamilton, G., et al. (2019) · Gastroenterology · doi:10.1053/j.gastro.2019.06.028 · Cited by 298 · see citing works
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INRICH: interval-based enrichment analysis for genome-wide association studies
Lee, P. H., O'Dushlaine, C., Thomas, B., & Purcell, S. M. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts191 · Cited by 238 · see citing works
Henriksen, P., Wagner, S. A., Weinert, B. T., Sharma, S., Bačinskaja, G., Rehman, M., Juffer, A. H., Walther, T. C., Lisby, M., & Choudhary, C. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.017251 · Cited by 246 · see citing works
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Pu, L., Naderi, M., Liu, T., Wu, H.-C., Mukhopadhyay, S., & Brylinski, M. (2019) · BMC Pharmacology and Toxicology · doi:10.1186/s40360-018-0282-6 · Cited by 212 · see citing works
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Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
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Fröhlich, F., Kessler, T., Weindl, D., Shadrin, A., Schmiester, L., Hache, H., Muradyan, A., Schütte, M., Lim, J.-H., Heinig, M., et al. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.013 · Cited by 143 · see citing works
Integration of metabolic and gene regulatory networks modulates the C. elegans dietary response
Watson, E., MacNeil, L. T., Arda, H. E., Zhu, L. J., & Walhout, A. J. M. (2013) · Cell · doi:10.1016/j.cell.2013.02.050 · Cited by 134 · see citing works
Amorim, A. M. B., Piochi, L. F., Gaspar, A. T., Preto, A. J., Rosário-Ferreira, N., & Moreira, I. S. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00352 · Cited by 132 · see citing works
Bioinformatics opportunities for identification and study of medicinal plants
Sharma, V., & Sarkar, I. N. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbs021 · Cited by 121 · see citing works
Genome-wide association study of severity in multiple sclerosis
International Multiple Sclerosis Genetics Consortium, Briggs, F. B. S., Shao, X., Goldstein, B. A., Oksenberg, J. R., Barcellos, L. F., & De Jager, P. L. (2011) · Genes & Immunity · doi:10.1038/gene.2011.34 · Cited by 120 · see citing works
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Bordbar, A., & Palsson, B. O. (2012) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2011.02494.x · Cited by 115 · see citing works
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Pietersen, C. Y., Mauney, S. A., Kim, S. S., Lim, M. P., Rooney, R. J., Goldstein, J. M., Petryshen, T. L., Seidman, L. J., Shenton, M. E., McCarley, R. W., et al. (2014) · Journal of Neurogenetics · doi:10.3109/01677063.2014.882918 · Cited by 87 · see citing works
Saini, D. K., Rai, A., Devi, A., Pabbi, S., Chhabra, D., Chang, J.-S., & Shukla, P. (2021) · Bioresource Technology · doi:10.1016/j.biortech.2021.124908 · Cited by 75 · see citing works
Jang, H. Y., Song, J., Kim, J. H., Lee, H., Kim, I.-W., Moon, B., & Oh, J. M. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00639-0 · Cited by 63 · see citing works
Laudanski, P., Charkiewicz, R., Tolwinska, A., Szamatowicz, J., Charkiewicz, A., & Niklinski, J. (2015) · BioMed Research International · doi:10.1155/2015/760698 · Cited by 48 · see citing works
Pulcrano, S., De Gregorio, R., De Sanctis, C., Volpicelli, F., Piscitelli, R. M., Speranza, L., Perrone-Capano, C., di Porzio, U., Caiazzo, M., Martini, A., et al. (2023) · The Journal of Neuroscience · doi:10.1523/jneurosci.0431-23.2023 · Cited by 12 · see citing works
The primary cilia are associated with the axon initial segment in neurons
Wang, H., Li, Y., Li, X., Sun, Z., Yu, F., Pashang, A., Kulasiri, D., Li, H. W., Chen, H., Hou, H., et al. (2025) · Advanced Science · doi:10.1002/advs.202407405 · Cited by 13 · see citing works
Guo, W., Gou, X., Yu, L., Zhang, Q., Yang, P., Pang, M., Pang, X., Pang, C., Wei, Y., & Zhang, X. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1129470 · Cited by 10 · see citing works
Yang, M., Li, S., Li, R., Dong, Y., Zhou, M., Zhao, J., Liu, M., & Tan, R. (2026) · New Biotechnology · doi:10.1016/j.nbt.2026.05.006
- Authors
- Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M., & Hirakawa, M.
- Affiliations
- Kyoto University (JP)
- Corresponding author
- Kanehisa, Minoru
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-10-30
- Volume
- 38
- Issue
- suppl_1
- Pages
- D355-D360
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC2808910
- OpenAlex
- W2171437346
- Cited by
- 2,322
- References
- 17
- Citations by year
- 2026: 31 · 2025: 72 · 2024: 87 · 2023: 79 · 2022: 88 · 2021: 108 · 2020: 89 · 2019: 100 · 2018: 111 · 2017: 105 · 2016: 131 · 2015: 126 · 2014: 151 · 2013: 215 · 2012: 322
- FWCI
- 111.11
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Ministry of Education, Culture, Sports, Science and Technology
- Japan Science and Technology Agency
- Institute for Chemical Research, Kyoto University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- KEGG, molecular networks, disease-associated genes, drug-target interactions, pathway maps, KEGG pathways, drug development history, metabolizing enzymes, traditional Chinese medicine formula
- MeSH terms
- Algorithms, Pharmaceutical Preparations, Humans, Ligands, Software, Genome, Human, Information Storage and Retrieval, Protein Structure, Tertiary, Computational Biology, Internet, Protein Interaction Mapping, Databases, Genetic, Databases, Nucleic Acid, Databases, Protein, Systems Biology
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