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.
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
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?
Data leakage has a significant impact, and Kapoor and Narayanan describe it as a reproducibility crisis in ML science.
“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]
The Introduction discusses KEGG as a manually created model in contrast to machine-learning models, but does not discuss data leakage or reproducibility. Searches for Kapoor and Narayanan, data leakage, and a machine-learning reproducibility crisis found no relevant passage in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIn their analysis, data leakage was detected in 15 of the 20 subject areas associated with biology or medicine.
“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]
The Introduction describes KEGG’s taxonomy-based analysis of pathways and genomes, not an analysis of data leakage. Searches for “data leakage” and “15 of the 20 subject areas” found no passage reporting the claimed result. The cited paper’s copy therefore does not verify the sentence. (LLM, conf=0.76)
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.
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 10,691 · see citing works
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 · see citing works
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 5,641 · see citing works
Toward understanding the origin and evolution of cellular organisms
Kanehisa, M. (2019) · Protein Science · doi:10.1002/pro.3715 · Cited by 5,509 · see citing works
Self-citationKEGG: integrating viruses and cellular organisms
Kanehisa, M. I., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M., & Tanabe, M. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa970 · Cited by 4,042 · see citing works
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 · see citing works
Federhen, S. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr1178 · Cited by 1,436 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Self-citationAssembly: a resource for assembled genomes at NCBI
Kitts, P. A., Church, D. M., Thibaud-Nissen, F., Choi, J., Hem, V., Sapojnikov, V., Smith, R. G., Tatusova, T., Xiang, C., Zherikov, A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1226 · Cited by 398 · see citing works
Sayers, E. W., Cavanaugh, M. L., Clark, K., Pruitt, K. D., Schoch, C. L., Sherry, S. T., & Karsch‐Mizrachi, I. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab1135 · Cited by 241 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The Gene Ontology knowledgebase in 2023
Feu, S. A. J. S. J. M. H. J. D. M. P. A. B. C. C. D. E., Aleksander, S. A., Balhoff, J., Carbon, S., Cherry, J., Drabkin, H., Ebert, D., Feuermann, M., Gaudet, P., Harris, N., et al. (2023) · Genetics · doi:10.1093/genetics/iyad031 · Cited by 2,726 · see citing works
KEGG: biological systems database as a model of the real world
Kanehisa, M. I., Furumichi, M., Sato, Y., Matsuura, Y., & Ishiguro-Watanabe, M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae909 · Cited by 2,634 · see citing works
Self-citationPang, Z., Lu, Y. P., Zhou, G., Hui, F., Xu, L., Viau, C., Spigelman, A. F., MacDonald, P. E., Wishart, D. S., Li, S., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae253 · Cited by 2,373 · see citing works
Kolberg, L., Raudvere, U., Kuzmin, I., Adler, P., Vilo, J., & Peterson, H. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad347 · Cited by 1,673 · see citing works
The Reactome Pathway Knowledgebase 2024
Orlic-Milacic, M., Beavers, D., Conley, P., Gong, C., Gillespie, M. E., Griss, J., Haw, R., Jassal, B., Matthews, L. R., May, B., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad1025 · Cited by 1,511 · see citing works
Zhao, L., Zhang, H., Li, N., Chen, J., Xu, H. N., Wang, Y., & Liang, Q. (2023) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2023.116306 · Cited by 986 · see citing works
The STRING database in 2025: protein networks with directionality of regulation
Szklarczyk, D., Nastou, K. C., Koutrouli, M., Kirsch, R. L., Mehryary, F., Hachilif, R., Hu, D., Peluso, M. E., Huang, Q. D., Fang, T., et al. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1113 · Cited by 856 · see citing works
TTD: Therapeutic Target Database describing target druggability information
Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., Wang, S., Qiu, Y., Chen, Y., & Zhu, F. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad751 · Cited by 667 · see citing works
OrthoDB and BUSCO update: annotation of orthologs with wider sampling of genomes
Tegenfeldt, F., Kuznetsov, D. A., Manni, M., Berkeley, M., Zdobnov, E. M., & Kriventseva, E. V. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae987 · Cited by 376 · see citing works
MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics
Pang, Z., Xu, L., Viau, C., Lu, Y. P., Salavati, R., Basu, N., & Xia, J. (2024) · Nature Communications · doi:10.1038/s41467-024-48009-6 · Cited by 296 · see citing works
ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization
Chen, Y., Mai, J., Cao, X., Burberry, A., Cominelli, F., & Zhang, L. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad470 · Cited by 286 · see citing works
Network pharmacology: a crucial approach in traditional Chinese medicine research
Zhai, Y., Liu, L., Zhang, F., Chen, X., Wang, H., Zhou, J., Chai, K., Liu, J., Lei, H., Lu, P., et al. (2025) · Chinese Medicine · doi:10.1186/s13020-024-01056-z · Cited by 281 · see citing works
Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
“KEGG for taxonomy-based analysis of pathways and genomes.”
MethodKong, X., Liu, C., Zhang, Z., Cheng, M., Mei, Z., Li, X., Liu, P., Diao, L., Ma, Y., Jiang, P., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad926 · Cited by 213 · see citing works
Progressive plasticity during colorectal cancer metastasis
Moorman, A., Benitez, E. K., Cambuli, F., Jiang, Q., Mahmoud, A., Lumish, M., Hartner, S., Balkaran, S., Bermeo, J., Asawa, S., et al. (2025) · Nature · doi:10.1038/s41586-024-08150-0 · Cited by 229 · see citing works
Rosati, D., Palmieri, M., Brunelli, G., Morrione, A., Iannelli, F., Frullanti, E., & Giordano, A. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.02.018 · Cited by 196 · see citing works
Galperin, M. Y., Alvarez, R. V., Karamycheva, S. A., Makarova, K. S., Wolf, Y. I., Landsman, D., & Koonin, E. V. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae983 · Cited by 154 · see citing works
Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources
Diamant, I., Clarke, D. J. B., Evangelista, J. E., Lingam, N., & Ma’ayan, A. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1080 · Cited by 149 · see citing works
Research Progress on the Pathogenesis of Knee Osteoarthritis
Du, X., Liu, Z., Tao, X., Mei, Y., Zhou, D., Cheng, K., Gao, S., Shi, H., Song, C., & Zhang, X. (2023) · Orthopaedic Surgery · doi:10.1111/os.13809 · Cited by 136 · see citing works
Genome-resolved metagenomics: a game changer for microbiome medicine
Kim, N., Ma, J., Kim, W., Kim, J., Belenky, P. A., & Lee, I. (2024) · Experimental & Molecular Medicine · doi:10.1038/s12276-024-01262-7 · Cited by 131 · see citing works
Protein corona formed on lipid nanoparticles compromises delivery efficiency of mRNA cargo
Voke, E., Arral, M. L., Squire, H. J., Lin, T.-J., Zheng, L., Coreas, R., Lui, A., Iavarone, A. T., Pinals, R. L., Whitehead, K. A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63726-2 · Cited by 146 · see citing works
The proteomic landscape of synaptic diversity across brain regions and cell types
van Oostrum, M., Blok, T. M., Giandomenico, S. L., tom Dieck, S., Tushev, G., Fürst, N., Langer, J. D., & Schuman, E. M. (2023) · Cell · doi:10.1016/j.cell.2023.09.028 · Cited by 135 · see citing works
Nishijima, S., Stankevič, E., Aasmets, O., Schmidt, T., Nagata, N., Keller, M. I., Ferretti, P., Juel, H. B., Fullam, A., Robbani, S. M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.10.022 · Cited by 127 · see citing works
Song, C., Zhou, Y., Cheng, K., Liu, F., Cai, W., Zhou, D., Chen, R., Shi, H., Fu, Z., Chen, J., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114711 · Cited by 121 · see citing works
Identification of the novel exhausted T cell CD8 + markers in breast cancer
Liu, H., Dong, A., Rasteh, A. M., Wang, P., & Weng, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-70184-1 · Cited by 121 · see citing works
Bile salt hydrolase in non-enterotoxigenic Bacteroides potentiates colorectal cancer
Sun, L., Zhang, Y., Cai, J., Rimal, B., Rocha, E. R., Coleman, J. P., Zhang, C., Nichols, R. G., Luo, Y., Kim, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36089-9 · Cited by 121 · see citing works
Li, X., Zhang, X.-T., Bie, X., Zhang, J., Jiang, D.-J., Tang, H., & Wang, F. (2024) · BMC Plant Biology · doi:10.1186/s12870-023-04695-w · Cited by 115 · see citing works
Comprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Lin, X., Dai, L., Zhou, Y., Yu, Z., Zhang, W., Shi, J., Cao, D., Li, Z., Chen, H., Song, B., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad235 · Cited by 121 · see citing works
Global organelle profiling reveals subcellular localization and remodeling at proteome scale
Hein, M. Y., Peng, D., Todorova, V., McCarthy, F., Kim, K., Liu, C., Savy, L., Januel, C., Baltazar-Nunez, R., Sekhar, M., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.028 · Cited by 113 · see citing works
Transcriptomics in the era of long-read sequencing
Monzó, C., Liu, T.-Y., & Conesa, A. (2025) · Nature reviews genetics · doi:10.1038/s41576-025-00828-z · Cited by 110 · see citing works
O’Connor, L. M., O’Connor, B. A., Lim, S. B., Zeng, J., & Lo, C. H. (2023) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2023.06.011 · Cited by 107 · see citing works
PomBase: a Global Core Biodata Resource—growth, collaboration, and sustainability
Rutherford, K., Lera-Ramírez, M., & Wood, V. (2024) · Genetics · doi:10.1093/genetics/iyae007 · Cited by 101 · see citing works
Evaluation of large language models for discovery of gene set function
Hu, M., Alkhairy, S., Lee, I., Pillich, R. T., Fong, D., Smith, K., Bachelder, R., Ideker, T., & Pratt, D. (2025) · Nature Methods · doi:10.1038/s41592-024-02525-x · Cited by 104 · see citing works
PANGEA: a new gene set enrichment tool for Drosophila and common research organisms
Hu, Y., Comjean, A., Attrill, H. L., Antonazzo, G., Thurmond, J., Chen, W., Li, F., Chao, T., Mohr, S. E., Brown, N. H., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad331 · Cited by 98 · see citing works
Baicalein induces apoptosis by inhibiting the glutamine-mTOR metabolic pathway in lung cancer
Li, J., Zhang, D., Wang, S., Yu, P., Sun, J., Zhang, Y., Meng, X., Li, J., & Li, X. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.02.023 · Cited by 99 · see citing works
Hartman, E., Scott, A. M., Karlsson, C. A. Q., Mohanty, T., Vaara, S. T., Linder, A., Malmström, L., & Malmström, J. A. (2023) · Nature Communications · doi:10.1038/s41467-023-41146-4 · Cited by 97 · see citing works
Mahdy, M. S., Azmy, A. F., Dishisha, T., Mohamed, W. R., Ahmed, K. A., Hassan, A. M., Aidy, S. E., & EL-Gendy, A. O. (2023) · BMC Microbiology · doi:10.1186/s12866-023-02791-3 · Cited by 96 · see citing works
Pathophysiology of chikungunya virus infection associated with fatal outcomes
de Souza, W. M., Fumagalli, M. J., de Lima, S. T. S., Parise, P. L., Carvalho, D. C. M., Hernandez, C., de Jesus, R., Delafiori, J., Cândido, D. D. S., Carregari, V. C., et al. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.02.011 · Cited by 96 · see citing works
Interrelation between gut microbiota, SCFA, and fatty acid composition in pigs
Sebastià, C., Folch, J. M., Ballester, M., Estellé, J., Passols, M., Muñoz, M., Casco, J. M. G., Fernández, A. I., Castelló, A., Sánchez, A., et al. (2023) · mSystems · doi:10.1128/msystems.01049-23 · Cited by 91 · see citing works
YTHDF2 promotes ATP synthesis and immune evasion in B cell malignancies
Chen, Z., Zeng, C., Yang, L., Che, Y., Chen, M., Sau, L., Wang, B., Zhou, K., Chen, Y., Qing, Y., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.007 · Cited by 88 · see citing works
Advancing microbial production through artificial intelligence-aided biology
Gong, X., Zhang, J., Gan, Q., Teng, Y., Hou, J., Lyu, Y., Liu, Z., Wu, Z., Dai, R., Zou, Y., et al. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108399 · Cited by 84 · see citing works
Bereczki, Z., Benczik, B., Balogh, O. M., Marton, S., Puhl, E., Pétervári, M., Váczy‐Földi, M., Papp, Z. T., Makkos, A., Glass, K., et al. (2024) · British Journal of Pharmacology · doi:10.1111/bph.17302 · Cited by 82 · see citing works
Phosphoenolpyruvate reallocation links nitrogen fixation rates to root nodule energy state
Ke, X., Xiao, H., Peng, Y., Wang, J., Lv, Q., & Wang, X. (2022) · Science · doi:10.1126/science.abq8591 · Cited by 80 · see citing works
Spatial transcriptomics reveals molecular dysfunction associated with cortical Lewy pathology
Goralski, T. M., Meyerdirk, L. K., Breton, L., Brasseur, L., Kurgat, K., DeWeerd, D., Turner, L., Becker, K., Adams, M., Newhouse, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47027-8 · Cited by 77 · see citing works
The landscape of drug sensitivity and resistance in sarcoma
Shihabi, A. A., Tebon, P. J., Nguyen, H. T. L., Chantharasamee, J., Sartini, S., Davarifar, A., Jensen, A. Y., Diaz-Infante, M. B., Cox, H., Gonzalez, A. E., et al. (2024) · Cell stem cell · doi:10.1016/j.stem.2024.08.010 · Cited by 75 · see citing works
Tzec-Interián, J. A., González‐Padilla, D., & Góngora‐Castillo, E. (2025) · Quantitative Biology · doi:10.1002/qub2.78 · Cited by 81 · see citing works
Eldosoky, M. A. E. R., Hammad, R., Elmadbouly, A. A. E., Aglan, R. B., AbdelHamid, S. G., Alboraie, M. A. A., Hassan, D. A., Shaheen, M. A. A., Rushdi, A., Ahmed, R. M., et al. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24043157 · Cited by 73 · see citing works
Harwood, D. S. L., Pedersen, V., Bager, N. S., Schmidt, A. Y., Stannius, T. O., Areškevičiūtė, A., Josefsen, K. E., Nørøxe, D. S., Scheie, D., Rostalski, H., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52167-y · Cited by 73 · see citing works
Akiyama, S., Nishijima, S., Kojima, Y., Kimura, M., Ohsugi, M., Ueki, K., Mizokami, M., Hattori, M., Tsuchiya, K., Uemura, N., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-54797-8 · Cited by 69 · see citing works
The role and application of bioinformatics techniques and tools in drug discovery
Zhang, S., Liu, K., Liu, Y., Hu, X., & Gu, X. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1547131 · Cited by 79 · see citing works
Lactococcus lactis in Dairy Fermentation—Health-Promoting and Probiotic Properties
Kondrotienė, K., Zavistanavičiūtė, P., Aksomaitienė, J., Novoslavskij, A., & Malakauskas, M. (2023) · Fermentation · doi:10.3390/fermentation10010016 · Cited by 76 · see citing works
Leveraging network pharmacology for drug discovery: Integrative approaches and emerging insights
Li, L., & Kar, S. (2025) · Medicine in Drug Discovery · doi:10.1016/j.medidd.2025.100220 · Cited by 67 · see citing works
B cells drive neuropathic pain–related behaviors in mice through IgG–Fc gamma receptor signaling
Lacagnina, M. J., Willcox, K. F., Boukelmoune, N., Bavencoffe, A., Sankaranarayanan, I., Barratt, D. T., Zuberi, Y. A., Dayani, D., Chavez, M. V., Lu, J. T., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adj1277 · Cited by 60 · see citing works
Bucci, L., Ghiotto, G., Zampieri, G., Raga, R., Favaro, L., Treu, L., & Campanaro, S. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c07737 · Cited by 67 · see citing works
GeneAgent: self-verification language agent for gene-set analysis using domain databases
Wang, Z., Jin, Q., Wei, C.-H., Tian, S., Lai, P., Zhu, Q., Day, C., Ross, C., Leaman, R., & Lu, Z. (2025) · Nature Methods · doi:10.1038/s41592-025-02748-6 · Cited by 79 · see citing works
Güleç, Ö., Duran, H. E., Arslan, M., Yıldıztekin, G., Ece, A., & Türkeş, C. (2025) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2025.108861 · Cited by 65 · see citing works
Exploring new galaxies: Perspectives on the discovery of novel PET-degrading enzymes
Mičan, J., Jaradat, D. M. M., Liu, W., Weber, G., Mazurenko, S., Bornscheuer, U. T., Damborský, J., Wei, R., & Bednář, D. (2023) · Applied Catalysis B: Environmental · doi:10.1016/j.apcatb.2023.123404 · Cited by 63 · see citing works
El-Sheikh, N. M., Abulsoud, A. I., Fawzy, A., Wasfey, E. F., & Hamdy, N. Μ. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154570 · Cited by 62 · see citing works
From 13 C-lignin to 13 C-mycelium: Agaricus bisporus uses polymeric lignin as a carbon source
Duran, K., Kohlstedt, M., van Erven, G., Klostermann, C. E., America, A. H. P., Bakx, E. J., Baars, J. J. P., Gorissen, A., de Visser, R., de Vries, R. P., et al. (2024) · Science Advances · doi:10.1126/sciadv.adl3419 · Cited by 62 · see citing works
Bacterial osmoprotectants—a way to survive in saline conditions and potential crop allies
Goszcz, A., Furtak, K., Stasiuk, R., Wójtowicz, J., Musiałowski, M., Schiavon, M., & Dębiec-Andrzejewska, K. (2025) · FEMS Microbiology Reviews · doi:10.1093/femsre/fuaf020 · Cited by 62 · see citing works
Merecz-Sadowska, A., Sadowski, A., Zielińska‐Bliźniewska, H., Zajdel, K., & Zajdel, R. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26146678 · Cited by 64 · see citing works
From multi-omics approaches to personalized medicine in myocardial infarction
Zhan, C., Tang, T., Wu, E., Zhang, Y., He, M., Wu, R., Bi, C., Wang, J., Zhang, Y., & Shen, B. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1250340 · Cited by 62 · see citing works
Schäfer, P. S. L., Dimitrov, D., Villablanca, E. J., & Sáez-Rodríguez, J. (2024) · Nature Immunology · doi:10.1038/s41590-024-01768-2 · Cited by 66 · see citing works
SoyMD: a platform combining multi-omics data with various tools for soybean research and breeding
Yang, Z., Luo, C., Pei, X., Wang, S., Huang, Y., Li, J., Liu, B., Kong, F., Yang, Q., & Fang, C. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad786 · Cited by 60 · see citing works
Yu, H., Yu, S., Guo, J., Wang, J., Mei, C., Raza, S. H. A., Cheng, G., & Zan, L. (2024) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.3c07844 · Cited by 60 · see citing works
Human papillomavirus E5 suppresses immunity via inhibition of the immunoproteasome and STING pathway
Miyauchi, S., Kim, S. S., Jones, R. N., Zhang, L., Guram, K., Sharma, S., Schoenberger, S. P., Cohen, E. E. W., Califano, J. A., & Sharabi, A. B. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112508 · Cited by 59 · see citing works
Zhang, J., Peng, G., Chi, H., Yang, J., Xie, X., Song, G., Tran, L. J., Xia, Z., & Tian, G. (2023) · BMC Oral Health · doi:10.1186/s12903-023-03138-0 · Cited by 59 · see citing works
Xie, Y., Shi, H., & Han, B. (2023) · BMC Pediatrics · doi:10.1186/s12887-023-03896-4 · Cited by 56 · see citing works
Meta-analysis of the effects of proton pump inhibitors on the human gut microbiota
Zhang, J., Zhang, C., Zhang, Q., Yu, L., Chen, W., Xue, Y., & Zhai, Q. (2023) · BMC Microbiology · doi:10.1186/s12866-023-02895-w · Cited by 65 · see citing works
Wu, M., Tao, H., Xu, T., Zheng, X., Wen, C., Wang, G., Peng, Y., & Dai, Y. (2024) · Proteome Science · doi:10.1186/s12953-024-00231-2 · Cited by 58 · see citing works
KEGG tools for classification and analysis of viral proteins
Jin, Z., Sato, Y., Kawashima, M., & Kanehisa, M. I. (2023) · Protein Science · doi:10.1002/pro.4820 · Cited by 55 · see citing works
Self-citationHan, Z., Zhang, Z., Yang, X., Li, Z., Sang, S., Islam, M. T., Guo, A. A., Li, Z., Wang, X., Wang, J., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-008927 · Cited by 55 · see citing works
The human zinc-binding cysteine proteome
Burger, N., Mittenbühler, M. J., Xiao, H., Shin, S.-H., Wei, S. M., Henze, E. K., Schindler, S., Mehravar, S., Wood, D. M., Petrocelli, J. J., et al. (2024) · Cell · doi:10.1016/j.cell.2024.11.025 · Cited by 58 · see citing works
Li, Y., Su, Z., Lin, Y., Xu, Z., Bao, H., Wang, F., Liu, J., Hu, S., Wang, Z., Yu, X., et al. (2024) · BMC Plant Biology · doi:10.1186/s12870-023-04712-y · Cited by 54 · see citing works
Compensatory evolution in NusG improves fitness of drug-resistant M. tuberculosis
Eckartt, K. A., Delbeau, M., Munsamy, V., DeJesus, M. A., Azadian, Z. A., Reddy, A. K., Chandanani, J., Poulton, N. C., Quiñones-García, S., Bosch, B., et al. (2024) · Nature · doi:10.1038/s41586-024-07206-5 · Cited by 54 · see citing works
Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes
Pekayvaz, K., Losert, C., Knottenberg, V., Gold, C., van Blokland, I. V., Oelen, R., Groot, H. E., Benjamins, J. W., Brambs, S., Kaiser, R. W. J., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02953-4 · Cited by 59 · see citing works
Tsenkova, M., Brauer, M., Pozdeev, V. I., Kasakin, M. F., Busi, S. B., Schmoetten, M., Cheung, D., Meyers, M., Rodriguez, F., Gaigneaux, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56678-0 · Cited by 54 · see citing works
Han, M., Cui, R., Wang, D., Huang, H., Rui, C., Malik, W. A., Wang, J., Zhang, H., Xu, N., Liu, X., et al. (2023) · BMC Plant Biology · doi:10.1186/s12870-023-04258-z · Cited by 52 · see citing works
Unveiling the human nitroproteome: Protein tyrosine nitration in cell signaling and cancer
Griswold‐Prenner, I., Kashyap, A. K., Mazhar, S., Hall, Z. W., Fazelinia, H., & Ischiropoulos, H. (2023) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2023.105038 · Cited by 52 · see citing works
Discovering nanoparticle corona ligands for liver macrophage capture
Bussin, B., Macduff, M. G. G., Ngo, W., Wu, J. L. Y., Lin, Z. P., Farias, A. G., Stordy, B. P., Sepahi, Z., Ahmed, S., Moffat, J., et al. (2025) · Nature Nanotechnology · doi:10.1038/s41565-025-01903-6 · Cited by 53 · see citing works
Hoti, D., Nixha, A. R., Duran, H., Arslan, M., Yıldıztekin, G., Ece, A., & Türkeş, C. (2025) · Bioorganic & Medicinal Chemistry · doi:10.1016/j.bmc.2025.118416 · Cited by 55 · see citing works
Discovery of disease-adapted bacterial lineages in inflammatory bowel diseases
Kumbhari, A., Cheng, T. N. H., Ananthakrishnan, A. N., Kochar, B., Burke, K. E., Shannon, K., Lau, H., Xavier, R. J., & Smillie, C. S. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.05.022 · Cited by 54 · see citing works
Metabolomics-based study of potential biomarkers of sepsis
Li, Y., Wang, C., & Chen, M. (2023) · Scientific Reports · doi:10.1038/s41598-022-24878-z · Cited by 49 · see citing works
Identifying potential biomarkers of idiopathic pulmonary fibrosis through machine learning analysis
Wu, Z., Chen, H., Ke, S., Mo, L., Qiu, M., Zhu, G., Zhu, W., & Liu, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-43834-z · Cited by 51 · see citing works
Wang, X., Shi, H., Huang, S., Zhang, Y., He, X., Long, Q., Qian, B., Zhong, Y., Qi, Z., Zhao, Q., et al. (2023) · Biomaterials · doi:10.1016/j.biomaterials.2023.122364 · Cited by 51 · see citing works
Liu, T., Qiao, H., Wang, Z., Yang, X., Pan, X., Yu, Y., Ye, X., Sakurai, T., Lin, H., & Zhang, Y. (2024) · Advanced Science · doi:10.1002/advs.202400009 · Cited by 51 · see citing works
A fin-loop-like structure in GPX4 underlies neuroprotection from ferroptosis
Lorenz, S., Wahida, A., Bostock, M. J., Seibt, T., Mourão, A., Levkina, A., Trümbach, D., Soudy, M., Emler, D., Rothammer, N., et al. (2025) · Cell · doi:10.1016/j.cell.2025.11.014 · Cited by 51 · see citing works
Wongfaed, N., O‐Thong, S., Sittijunda, S., & Reungsang, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-29895-0 · Cited by 50 · see citing works
Yu, H., Wang, J., Zhang, K., Cheng, G., Mei, C., & Zan, L. (2023) · BMC Genomics · doi:10.1186/s12864-023-09452-9 · Cited by 50 · see citing works
Hintikka, J. E., Ahtiainen, J. P., Permi, P., Jalkanen, S., Lehtonen, M., & Pekkala, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-38357-6 · Cited by 50 · see citing works
Nutritional Characteristics, Health Impact, and Applications of Kefir
Apalowo, O. E., Adegoye, G. A., Mbogori, T. N., Kandiah, J., & Obuotor, T. M. (2024) · Foods · doi:10.3390/foods13071026 · Cited by 53 · see citing works
Li, V. O. K., Yang, H., Kaistha, T., Zhang, Q., Downey, J., Gozes, I., & Lam, J. C. K. (2025) · Scientific Reports · doi:10.1038/s41598-025-85947-7 · Cited by 57 · see citing works
Liu, S., Liu, Z., Shang, A., Xun, J., Lv, Z., Zhou, S., Liu, C., Zhang, Q., & Yang, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-33915-4 · Cited by 48 · see citing works
Naithani, S., Deng, C. H., Sahu, S. K., & Jaiswal, P. (2023) · Biomolecules · doi:10.3390/biom13091403 · Cited by 48 · see citing works
Comprehensive multi-omics analysis of breast cancer reveals distinct long-term prognostic subtypes
Sharma, A., Debik, J., Naume, B., Ohnstad, H. O., Sahlberg, K. K., Borgen, E., Børresen‐Dale, A., Engebråten, O., Fritzman, B., Garred, Ø., et al. (2024) · Oncogenesis · doi:10.1038/s41389-024-00521-6 · Cited by 48 · see citing works
Wang, C., Yang, C., Western, D., Ali, M., Wang, Y., Phuah, C., Budde, J., Wang, L., Gorijala, P., Timsina, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01973-7 · Cited by 51 · see citing works
Aspartame carcinogenic potential revealed through network toxicology and molecular docking insights
Chen, D., & Hou, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-62461-w · Cited by 47 · see citing works
Whole-genome duplication in an algal symbiont bolsters coral heat tolerance
Dougan, K. E., Bellantuono, A. J., Kahlke, T., Abbriano, R. M., Chen, Y., Shah, S., Granados‐Cifuentes, C., van Oppen, M. J. H., Bhattacharya, D., Suggett, D. J., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn2218 · Cited by 48 · see citing works
Zhang, J., Chen, B., Li, H., Wang, Y., Liu, X., Wong, K. Y., Chan, W. N., Chan, A. K., Cheung, A. H. K., Leung, K. T., et al. (2022) · The Journal of Pathology · doi:10.1002/path.6033 · Cited by 46 · see citing works
Xu, Z., Yang, L., Pi, P., Yi, Y., Tang, H., Zhang, Z., Xiong, H., Lei, B., Shi, Y., Li, J., et al. (2024) · Phytomedicine · doi:10.1016/j.phymed.2024.155817 · Cited by 46 · see citing works
- 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 · 2019: 2
- 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
