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Artículos de revistas sobre el tema "KEGG"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 26 de julio de 2025

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1

Manyam, Ganiraju, Aybike Birerdinc, and Ancha Baranova. "KPP: KEGG Pathway Painter." BMC Systems Biology 9, Suppl 2 (2015): S3. http://dx.doi.org/10.1186/1752-0509-9-s2-s3.

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2

Sultana, Kazi Zakia, Anupam Bhattacharjee, and Hasan Jamil. "Querying KEGG pathways in logic." International Journal of Data Mining and Bioinformatics 9, no. 1 (2014): 1. http://dx.doi.org/10.1504/ijdmb.2014.057772.

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3

Kanehisa, M. "The KEGG databases at GenomeNet." Nucleic Acids Research 30, no. 1 (2002): 42–46. http://dx.doi.org/10.1093/nar/30.1.42.

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4

Kanehisa, Minoru, Miho Furumichi, Yoko Sato, Mari Ishiguro-Watanabe, and Mao Tanabe. "KEGG: integrating viruses and cellular organisms." Nucleic Acids Research 49, no. D1 (2020): D545—D551. http://dx.doi.org/10.1093/nar/gkaa970.

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Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which

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5

Hashimoto, Kosuke, Susumu Goto, Shin Kawano, et al. "KEGG as a glycome informatics resource." Glycobiology 16, no. 5 (2006): 63R—70R. http://dx.doi.org/10.1093/glycob/cwj010.

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6

Ogata, Hiroyuki, Susumu Goto, Wataru Fujibuchi, and Minoru Kanehisa. "Computation with the KEGG pathway database." Biosystems 47, no. 1-2 (1998): 119–28. http://dx.doi.org/10.1016/s0303-2647(98)00017-3.

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7

Slizen, Mikhail V., and Oxana V. Galzitskaya. "Comparative Analysis of Proteomes of a Number of Nosocomial Pathogens by KEGG Modules and KEGG Pathways." International Journal of Molecular Sciences 21, no. 21 (2020): 7839. http://dx.doi.org/10.3390/ijms21217839.

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Nosocomial (hospital-acquired) infections remain a serious challenge for health systems. The reason for this lies not only in the local imperfection of medical practices and protocols. The frequency of infection with antibiotic-resistant strains of bacteria is growing every year, both in developed and developing countries. In this work, a pangenome and comparative analysis of 201 genomes of Staphylococcus aureus, Enterobacter spp., Pseudomonas aeruginosa, and Mycoplasma spp. was performed on the basis of high-level functional annotations—KEGG pathways and KEGG modules. The first three organism

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8

Zhang, Chao, Zhongwei Chen, Miming Zhang, and Shulei Jia. "KEGG_Extractor: An Effective Extraction Tool for KEGG Orthologs." Genes 14, no. 2 (2023): 386. http://dx.doi.org/10.3390/genes14020386.

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The KEGG Orthology (KO) database is a widely used molecular function reference database which can be used to conduct functional annotation of most microorganisms. At present, there are many KEGG tools based on the KO entries for annotating functional orthologs. However, determining how to efficiently extract and sort the annotation results of KEGG still hinders the subsequent genome analysis. There is a lack of effective measures used to quickly extract and classify the gene sequences and species information of the KEGG annotations. Here, we present a supporting tool: KEGG_Extractor for specie

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9

Kanehisa, M. "The KEGG resource for deciphering the genome." Nucleic Acids Research 32, no. 90001 (2004): 277D—280. http://dx.doi.org/10.1093/nar/gkh063.

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10

Ogata, H., S. Goto, K. Sato, W. Fujibuchi, H. Bono, and M. Kanehisa. "KEGG: Kyoto Encyclopedia of Genes and Genomes." Nucleic Acids Research 27, no. 1 (1999): 29–34. http://dx.doi.org/10.1093/nar/27.1.29.

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11

Kanehisa, M. "KEGG: Kyoto Encyclopedia of Genes and Genomes." Nucleic Acids Research 28, no. 1 (2000): 27–30. http://dx.doi.org/10.1093/nar/28.1.27.

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12

Nersisyan, Lilit, Ruben Samsonyan, and Arsen Arakelyan. "CyKEGGParser: tailoring KEGG pathways to fit into systems biology analysis workflows." F1000Research 3 (August 14, 2014): 145. http://dx.doi.org/10.12688/f1000research.4410.2.

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The KEGG pathway database is a widely accepted source for biomolecular pathway maps. In this paper we present the CyKEGGParser app (http://apps.cytoscape.org/apps/cykeggparser) for Cytoscape 3 that allows manipulation with KEGG pathway maps. Along with basic functionalities for pathway retrieval, visualization and export in KGML and BioPAX formats, the app provides unique features for computer-assisted adjustment of inconsistencies in KEGG pathway KGML files and generation of tissue- and protein-protein interaction specific pathways. We demonstrate that using biological context-specific KEGG p

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13

Aoki-Kinoshita, Kiyoko F. "Overview of KEGG applications to omics-related research." Journal of Pesticide Science 31, no. 3 (2006): 296–99. http://dx.doi.org/10.1584/jpestics.31.296.

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14

Villaveces, Jose M., Rafael C. Jimenez, and Bianca H. Habermann. "KEGGViewer, a BioJS component to visualize KEGG Pathways." F1000Research 3 (February 13, 2014): 43. http://dx.doi.org/10.12688/f1000research.3-43.v1.

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Summary: Signaling pathways provide essential information on complex regulatory processes within the cell. They are moreover widely used to interpret and integrate data from large-scale studies, such as expression or functional screens. We present KEGGViewer a BioJS component to visualize KEGG pathways and to allow their visual integration with functional data. Availability: KEGGViewer is an open-source tool freely available at the BioJS Registry. Instructions on how to use the tool are available at http://goo.gl/dVeWpg and the source code can be found at http://github.com/biojs/biojs and DOI:

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15

Klukas, C., and F. Schreiber. "Dynamic exploration and editing of KEGG pathway diagrams." Bioinformatics 23, no. 3 (2006): 344–50. http://dx.doi.org/10.1093/bioinformatics/btl611.

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16

Cui, Weiren, Lei Chen, Tao Huang, et al. "Computationally identifying virulence factors based on KEGG pathways." Molecular BioSystems 9, no. 6 (2013): 1447. http://dx.doi.org/10.1039/c3mb70024k.

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17

Kanehisa, Minoru, Yoko Sato, Miho Furumichi, Kanae Morishima, and Mao Tanabe. "New approach for understanding genome variations in KEGG." Nucleic Acids Research 47, no. D1 (2018): D590—D595. http://dx.doi.org/10.1093/nar/gky962.

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18

Aramaki, Takuya, Romain Blanc-Mathieu, Hisashi Endo, et al. "KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold." Bioinformatics 36, no. 7 (2019): 2251–52. http://dx.doi.org/10.1093/bioinformatics/btz859.

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Abstract Summary KofamKOALA is a web server to assign KEGG Orthologs (KOs) to protein sequences by homology search against a database of profile hidden Markov models (KOfam) with pre-computed adaptive score thresholds. KofamKOALA is faster than existing KO assignment tools with its accuracy being comparable to the best performing tools. Function annotation by KofamKOALA helps linking genes to KEGG resources such as the KEGG pathway maps and facilitates molecular network reconstruction. Availability and implementation KofamKOALA, KofamScan and KOfam are freely available from GenomeNet (https://

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19

Wrzodek, Clemens, Finja Büchel, Manuel Ruff, Andreas Dräger, and Andreas Zell. "Precise generation of systems biology models from KEGG pathways." BMC Systems Biology 7, no. 1 (2013): 15. http://dx.doi.org/10.1186/1752-0509-7-15.

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20

Kanehisa, Minoru, Miho Furumichi, Mao Tanabe, Yoko Sato, and Kanae Morishima. "KEGG: new perspectives on genomes, pathways, diseases and drugs." Nucleic Acids Research 45, no. D1 (2016): D353—D361. http://dx.doi.org/10.1093/nar/gkw1092.

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21

Mo, Sen, Chong Liu, Liyi Chen, et al. "KEGG-expressed genes and pathways in intervertebral disc degeneration." Medicine 98, no. 21 (2019): e15796. http://dx.doi.org/10.1097/md.0000000000015796.

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22

Yeheskel, Adva, Adam Reiter, Metsada Pasmanik-Chor, and Amir Rubinstein. "Simulation and visualization of multiple KEGG pathways using BioNSi." F1000Research 6 (December 11, 2017): 2120. http://dx.doi.org/10.12688/f1000research.13254.1.

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Motivation: Many biologists are discouraged from using network simulation tools because these require manual, often tedious network construction. This situation calls for building new tools or extending existing ones with the ability to import biological pathways previously deposited in databases and analyze them, in order to produce novel biological insights at the pathway level. Results: We have extended a network simulation tool (BioNSi), which now allows merging of multiple pathways from the KEGG pathway database into a single, coherent network, and visualizing its properties. Furthermore,

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23

Yeheskel, Adva, Adam Reiter, Metsada Pasmanik-Chor, and Amir Rubinstein. "Simulation and visualization of multiple KEGG pathways using BioNSi." F1000Research 6 (May 14, 2018): 2120. http://dx.doi.org/10.12688/f1000research.13254.2.

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Motivation: Many biologists are discouraged from using network simulation tools because these require manual, often tedious network construction. This situation calls for building new tools or extending existing ones with the ability to import biological pathways previously deposited in databases and analyze them, in order to produce novel biological insights at the pathway level. Results: We have extended a network simulation tool (BioNSi), which now allows merging of multiple pathways from the KEGG pathway database into a single, coherent network, and visualizing its properties. Furthermore,

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24

Kanehisa, M., M. Araki, S. Goto, et al. "KEGG for linking genomes to life and the environment." Nucleic Acids Research 36, Database (2007): D480—D484. http://dx.doi.org/10.1093/nar/gkm882.

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25

Okuda, S., T. Yamada, M. Hamajima, et al. "KEGG Atlas mapping for global analysis of metabolic pathways." Nucleic Acids Research 36, Web Server (2008): W423—W426. http://dx.doi.org/10.1093/nar/gkn282.

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26

Arakelyan, Arsen, and Lilit Nersisyan. "KEGGParser: parsing and editing KEGG pathway maps in Matlab." Bioinformatics 29, no. 4 (2013): 518–19. http://dx.doi.org/10.1093/bioinformatics/bts730.

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27

Kanehisa, M. "From genomics to chemical genomics: new developments in KEGG." Nucleic Acids Research 34, no. 90001 (2006): D354—D357. http://dx.doi.org/10.1093/nar/gkj102.

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28

Kanehisa, Minoru, and Yoko Sato. "KEGG Mapper for inferring cellular functions from protein sequences." Protein Science 29, no. 1 (2019): 28–35. http://dx.doi.org/10.1002/pro.3711.

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29

Liu, Yi, Bobin Mi, Huijuan Lv, et al. "Shared KEGG pathways of icariin‐targeted genes and osteoarthritis." Journal of Cellular Biochemistry 120, no. 5 (2018): 7741–50. http://dx.doi.org/10.1002/jcb.28048.

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30

Arakawa, Kazuharu, Nobuaki Kono, Yohei Yamada, Hirotada Mori, and Masaru Tomita. "KEGG-Based Pathway Visualization Tool for Complex Omics Data." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 5, no. 4 (2005): 419–23. https://doi.org/10.3233/isb-00199.

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Pathway-level visualization of omics data provides an essential means for systems biology, to capture the systematic properties of the inner activities of cells. Here we describe a web-based resource consisting of a web-application for the visualization of complex omics data onto KEGG pathways to overview all entities in the context of cellular pathways, and databases created with the software to visualize a series of microarray data. The web-application accepts transcriptome, proteome, metabolome, or the combination of these data as input, and because of this scalability it is advantageous fo

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31

David, Chaim, and Haridimos Kondylakis. "KEGGSum: Summarizing Genomic Pathways." Information 15, no. 1 (2024): 56. http://dx.doi.org/10.3390/info15010056.

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Over time, the renowned Kyoto Encyclopedia of Genes and Genomes (KEGG) has grown to become one of the most comprehensive online databases for biological procedures. The majority of the data are stored in the form of pathways, which are graphs that depict the relationships between the diverse items participating in biological procedures, such as genes and chemical compounds. However, the size, complexity, and diversity of these graphs make them difficult to explore and understand, as well as making it difficult to extract a clear conclusion regarding their most important components. In this reg

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32

Pian, Cong, Guangle Zhang, Libin Gao, Xiaodan Fan, and Fei Li. "miR+Pathway: the integration and visualization of miRNA and KEGG pathways." Briefings in Bioinformatics 21, no. 2 (2019): 699–708. http://dx.doi.org/10.1093/bib/bby128.

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Abstract miRNAs represent a type of noncoding small molecule RNA. Many studies have shown that miRNAs are widely involved in the regulation of various pathways. The key to fully understanding the regulatory function of miRNAs is the determination of the pathways in which the miRNAs participate. However, the major pathway databases such as KEGG only include information regarding protein-coding genes. Here, we redesigned a pathway database (called miR+Pathway) by integrating and visualizing the 8882 human experimentally validated miRNA-target interactions (MTIs) and 150 KEGG pathways. This datab

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33

Chanumolu, Sree K., Mustafa Albahrani, Handan Can, and Hasan H. Otu. "KEGG2Net: Deducing gene interaction networks and acyclic graphs from KEGG pathways." EMBnet.journal 26 (March 5, 2021): e949. http://dx.doi.org/10.14806/ej.26.0.949.

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The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database provides a manual curation of biological pathways that involve genes (or gene products), metabolites, chemical compounds, maps, and other entries. However, most applications and datasets involved in omics are gene or protein-centric requiring pathway representations that include direct and indirect interactions only between genes. Furthermore, special methodologies, such as Bayesian networks, require acyclic representations of graphs. We developed KEGG2Net, a web resource that generates a network involving only the genes repre

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34

Huckvale, Erik D., and Hunter N. B. Moseley. "A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement." PLOS ONE 19, no. 5 (2024): e0299583. http://dx.doi.org/10.1371/journal.pone.0299583.

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The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to predict these metabolite-to-pathway mappings, utilizing a training dataset of known metabolite-to-pathway mappings. A few such training datasets have been derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG). However, several prior published machine learning approaches utilized an erroneous KEGG-derived training dataset that used SMILES mol

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35

Seo, Dongmin, Min-Ho Lee, and Seok Yu. "Development of Network Analysis and Visualization System for KEGG Pathways." Symmetry 7, no. 3 (2015): 1275–88. http://dx.doi.org/10.3390/sym7031275.

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36

Kanehisa, Minoru, Susumu Goto, Yoko Sato, Masayuki Kawashima, Miho Furumichi, and Mao Tanabe. "Data, information, knowledge and principle: back to metabolism in KEGG." Nucleic Acids Research 42, no. D1 (2013): D199—D205. http://dx.doi.org/10.1093/nar/gkt1076.

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37

Altman, Tomer, Michael Travers, Anamika Kothari, Ron Caspi, and Peter D. Karp. "A systematic comparison of the MetaCyc and KEGG pathway databases." BMC Bioinformatics 14, no. 1 (2013): 112. http://dx.doi.org/10.1186/1471-2105-14-112.

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38

Wylie, Todd, John Martin, Sahar Abubucker, et al. "NemaPath: online exploration of KEGG-based metabolic pathways for nematodes." BMC Genomics 9, no. 1 (2008): 525. http://dx.doi.org/10.1186/1471-2164-9-525.

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39

Kanehisa, Minoru, Yoko Sato, Masayuki Kawashima, Miho Furumichi, and Mao Tanabe. "KEGG as a reference resource for gene and protein annotation." Nucleic Acids Research 44, no. D1 (2015): D457—D462. http://dx.doi.org/10.1093/nar/gkv1070.

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40

Yu, Tao, Yuan Xiong, Simon Luu, et al. "The shared KEGG pathways between icariin-targeted genes and osteoporosis." Aging 12, no. 9 (2020): 8191–201. http://dx.doi.org/10.18632/aging.103133.

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41

Chen, Jiarui, Chong Liu, Jiemei Cen, et al. "KEGG-expressed genes and pathways in triple negative breast cancer." Medicine 99, no. 18 (2020): e19986. http://dx.doi.org/10.1097/md.0000000000019986.

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42

Kono, Nobuaki, Kazuharu Arakawa, and Masaru Tomita. "MEGU: Pathway Mapping Web-Service Based on KEGG and SVG." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 6, no. 6 (2006): 621–25. https://doi.org/10.3233/isb-00270.

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Scientific visualization is a key technology to enhance our understanding of huge masses of omics data, especially for cell-wide, high-throughput measurements of the physiological networks. We have developed a web application that visualizes complex omics data of multiple layers simultaneously, including transcriptome, proteome, and metabolome, onto an integrated pathway diagram derived by connecting the individual KEGG pathway maps; the mapped images are generated in Scalable Vector Graphics for easy editing by hand or with computer programs or drawing software. The web application is availab

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43

Luo, YuanYuan, Yan Yan, Shiqi Zhang, and Zhen Li. "Computational Approach to Investigating Key GO Terms and KEGG Pathways Associated with CNV." BioMed Research International 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/8406857.

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Choroidal neovascularization (CNV) is a severe eye disease that leads to blindness, especially in the elderly population. Various endogenous and exogenous regulatory factors promote its pathogenesis. However, the detailed molecular biological mechanisms of CNV have not been fully revealed. In this study, by using advanced computational tools, a number of key gene ontology (GO) terms and KEGG pathways were selected for CNV. A total of 29 validated genes associated with CNV and 17,639 nonvalidated genes were encoded based on the features derived from the GO terms and KEGG pathways by using the e

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44

Du, Junli, Zhifa Yuan, Ziwei Ma, Jiuzhou Song, Xiaoli Xie, and Yulin Chen. "KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model." Mol. BioSyst. 10, no. 9 (2014): 2441–47. http://dx.doi.org/10.1039/c4mb00287c.

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The KEGG-PATH approach, a kind of data mining through functional enrichment analysis of time-course experiments or those involving multiple treatments, can uncover the complex regulation mechanisms of KEGG pathways through the subdivision of total effect.

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45

Jamialahmadi, Oveis, Ehsan Motamedian, and Sameereh Hashemi-Najafabadi. "BiKEGG: a COBRA toolbox extension for bridging the BiGG and KEGG databases." Molecular BioSystems 12, no. 11 (2016): 3459–66. http://dx.doi.org/10.1039/c6mb00532b.

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46

Yin, Hang, ShaoPeng Wang, Yu-Hang Zhang, Yu-Dong Cai, and Hailin Liu. "Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer." BioMed Research International 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7861274.

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Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO t

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47

Huckvale, Erik D., Christian D. Powell, Huan Jin, and Hunter N. B. Moseley. "Benchmark Dataset for Training Machine Learning Models to Predict the Pathway Involvement of Metabolites." Metabolites 13, no. 11 (2023): 1120. http://dx.doi.org/10.3390/metabo13111120.

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Metabolic pathways are a human-defined grouping of life sustaining biochemical reactions, metabolites being both the reactants and products of these reactions. But many public datasets include identified metabolites whose pathway involvement is unknown, hindering metabolic interpretation. To address these shortcomings, various machine learning models, including those trained on data from the Kyoto Encyclopedia of Genes and Genomes (KEGG), have been developed to predict the pathway involvement of metabolites based on their chemical descriptions; however, these prior models are based on old meta

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48

Yuan, Fei, Xiaoyong Pan, Lei Chen, Yu-Hang Zhang, Tao Huang, and Yu-Dong Cai. "Analysis of Protein–Protein Functional Associations by Using Gene Ontology and KEGG Pathway." BioMed Research International 2019 (July 18, 2019): 1–10. http://dx.doi.org/10.1155/2019/4963289.

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Protein–protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein–protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein–protein functional associations validated by experime

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49

Zhang, Jian, ZhiHao Xing, Mingming Ma, et al. "Gene Ontology and KEGG Enrichment Analyses of Genes Related to Age-Related Macular Degeneration." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/450386.

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Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores

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50

Jamialahmadi, Oveis, Ehsan Motamedian, and Sameereh Hashemi-Najafabadi. "Correction: BiKEGG: a COBRA toolbox extension for bridging the BiGG and KEGG databases." Molecular BioSystems 12, no. 12 (2016): 3743. http://dx.doi.org/10.1039/c6mb90040b.

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