The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
KEGG for taxonomy-based analysis of pathways and genomes
Open accessAbstract
KEGG (https://www.kegg.jp) is a manually curated database resource integrating various biological objects categorized into systems, genomic, chemical and health information. Each object (database entry) is identified by the KEGG identifier (kid), which generally takes the form of a prefix followed by a five-digit number, and can be retrieved by appending /entry/kid in the URL. The KEGG pathway map viewer, the Brite hierarchy viewer and the newly released KEGG genome browser can be launched by appending /pathway/kid, /brite/kid and /genome/kid, respectively, in the URL. Together with an improved annotation procedure for KO (KEGG Orthology) assignment, an increasing number of eukaryotic genomes have been included in KEGG for better representation of organisms in the taxonomic tree. Multiple taxonomy files are generated for classification of KEGG organisms and viruses, and the Brite hierarchy viewer is used for taxonomy mapping, a variant of Brite mapping in the new KEGG Mapper suite. The taxonomy mapping enables analysis of, for example, how functional links of genes in the pathway and physical links of genes on the chromosome are conserved among organism groups.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Data leakage has a significant impact, and Kapoor and Narayanan describe it as a reproducibility crisis in ML science (76).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [76]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“In their analysis, data leakage was detected in 15 of the 20 subject areas associated with biology or medicine.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [76]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Database resources of the National Center for Biotechnology Information: update
Wheeler, D. L. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh073 · Cited by 10,695
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357
Whole-Genome Random Sequencing and Assembly of Haemophilus influenzae Rd
Fleischmann, R. D., Adams, M. D., White, O. R., Clayton, R. A., Kirkness, E. F., Kerlavage, A. R., Bult, C. J., Tomb, J.-F., Dougherty, B. A., Merrick, J. M., et al. (1995) · Science · doi:10.1126/science.7542800 · Cited by 5,731
Toward understanding the origin and evolution of cellular organisms
Kanehisa, M. (2019) · Protein Science · doi:10.1002/pro.3715 · Cited by 5,509 · cited 2×
“It is hoped that this latter aspect will further be improved to enable a better understanding of more fundamental problems, such as how molecular network systems were developed in the cell, coevolved with the genome and transmitted to extant species (3).”
BackgroundSelf-citationKEGG: integrating viruses and cellular organisms
Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M., & Tanabe, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa970 · Cited by 3,436
Self-citationDatabase resources of the national center for biotechnology information
Sayers, E. W., Bolton, E. E., Brister, J. R., Canese, K., Chan, J., Comeau, D. C., Connor, R., Funk, K., Kelly, C., Kim, S., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1112 · Cited by 2,812
Federhen, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1178 · Cited by 1,436
Virus taxonomy: the database of the International Committee on Taxonomy of Viruses (ICTV)
Lefkowitz, E. J., Dempsey, D. M., Hendrickson, R. C., Orton, R. J., Siddell, S. G., & Smith, D. B. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx932 · Cited by 1,230
Metabolomics: A New Tool to Reveal the Nature of Diabetic Kidney Disease
Huang, G., Li, M., Li, Y. J., & Mao, Y. (2022) · Laboratory Medicine · doi:10.1093/labmed/lmac041 · Cited by 933
KEGG mapping tools for uncovering hidden features in biological data
Kanehisa, M. I., Sato, Y., & Kawashima, M. (2021) · Protein Science · doi:10.1002/pro.4172 · Cited by 749
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 1,001–1,010 of 1,010
Long, T. E., Naidu, S. T., Hissom, E. G., Meka, Y., Chavva, H., Brown, K. C., Valentine, M. E., Fan, J., Denvir, J., Primerano, D. A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-00078-3 · Cited by 8
Amin, W., Enam, S. A., Sufiyan, S., Ghias, K., Bajwa, M. H., Ilyas, S., Laghari, A. A., Naeem, S., Abidi, S. H., & Mughal, N. (2024) · PLOS ONE · doi:10.1371/journal.pone.0311308 · Cited by 8
Autophagy dark genes: Can we find them with machine learning?
Ranjbar, M., Yang, J. J., Kumar, P., Byrd, D. R., Bearer, E. L., & Oprea, T. I. (2023) · Natural Sciences · doi:10.1002/ntls.20220067 · Cited by 7
Huang, X., Liu, Y., Wang, Q., Rehman, H. M., Horváth, D., Zhou, S., Fu, R., Zhang, L., Szöllősi, A. G., & Li, Z. (2023) · BMC Oral Health · doi:10.1186/s12903-023-03181-x · Cited by 6
“Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment were carried out [15, 16].”
RetractedLu, W., Zha, B., Lyu, J., LingHu, C., Chen, J., Deng, S., Zhang, X., Li, L., & Wang, G. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1500886 · Cited by 4
Zhong, J., Li, M., Dai, Z., & Wan, J. (2025) · Neurochemistry International · doi:10.1016/j.neuint.2025.106049 · Cited by 3
Laghari, A. A., Sufiyan, S., Amin, W., Ilyas, S., Naeem, S., Adnan, U., Abidi, S. H., Uddin, S., Enam, S. A., & Mughal, N. (2026) · Journal of Neuro-Oncology · doi:10.1007/s11060-025-05287-4 · Cited by 1
Li, Q., Nichols, C., Welner, R. S., Chen, J. Y., Ku, W.-S., & Yue, Z. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag244
“…has significantly enhanced the analytical power, enabling comprehensive analysis from various sources, including pathways (Reactome [ 44], KEGG [ 45], BioCarta, WikiPathways [ 46], DMaps [ 47], SPIKE, NCI Nature Curated pathway [ 48], and GeoMx Cancer Transcriptome Atlas [ 49]), ontology…”
Selleri, E., Longhi, G., Tarracchini, C., Carolis, E. D., Petraro, S., Mancabelli, L., Milani, C., Turroni, F., Ventura, M., & Lugli, G. A. (2026) · Microbiome Research Reports · doi:10.20517/mrr.2026.19
scTraitReg: a single-cell multi-omic database of trait-relevant transcription factor eRegulons
Chen, H., Liu, Y., Rui, X., Li, S., Sun, L., Xiao, G., Tang, H., Sun, Y., Guan, X., & Cao, C. (2026) · Nucleic Acids Research · doi:10.1093/nar/gkag1017
Metadata
- Authors
- Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M., & Ishiguro-Watanabe, M.
- Affiliations
- Kyoto University (JP)
- Fujitsu (Japan) (JP)
- Network Support Co. Ltd., Hakata-ku , Fukuoka 812-0011, Japan
- Institute of Medical Science, The University of Tokyo (JP)
- Corresponding author
- Kanehisa, Minoru
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-10-27
- Volume
- 51
- Issue
- D1
- Pages
- D587-D592
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC9825424
- OpenAlex
- W4307425304
- Cited by
- 5,833
- References
- 12
- Citations by year
- 2026: 911 · 2025: 1421 · 2024: 1899 · 2023: 1560 · 2022: 39
- FWCI
- 461.30
- 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)
- Institutional Repositories DataBase (IRDB) (submittedVersion)
- Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by-nc)
- Funders
- Japan Science and Technology Agency
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- KEGG, pathway maps, genome annotation, KEGG Orthology, eukaryotic genomes, viruses, genome browser
- MeSH terms
- Databases, Factual, Genome, Genomics, Databases, Genetic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
