Bibliografías temáticas / KEGG

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Literatura académica sobre el tema "KEGG"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 16 de junio de 2025

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Artículos de revistas sobre el tema "KEGG"

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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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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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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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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 are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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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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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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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 organisms are serious nosocomial pathogens, often exhibiting multidrug resistance. Analysis of KEGG modules revealed methicillin resistance in 25% of S. aureus strains and resistance to carbapenems in 21% of Enterobacter spp. strains. P. aeruginosa has a wide range of unique efflux systems. One hundred percent of the analyzed strains have at least two drug resistance systems, and 75% of the strains have seven. Each of the organisms has a characteristic set of metabolic features, whose impact on drug resistance can be considered in future studies. Comparing the genomes of nosocomial pathogens with each other and with Mycoplasma genomes can expand our understanding of the versatility of certain metabolic features and mechanisms of drug resistance.
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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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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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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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Tesis sobre el tema "KEGG"

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Rajasimha, Harsha Karur. "PathMeld: A Methodology for The Unification of Metabolic Pathway Databases." Thesis, Virginia Tech, 2004. http://hdl.handle.net/10919/36325.

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A biological pathway database is a database that describes biochemical pathways, reactions, enzymes that catalyze the reactions, and the substrates that participate in these reactions. A pathway genome database (PGDB) integrates pathway information with information about the complete genome of various sequenced organisms. Two of the popular PGDBs available today are the Kyoto Encyclopedia of Genes and Genomes (KEGG) and MetaCyc. The proliferation of biological databases in general raises several questions for the life scientist. Which of these databases is most accurate, most current, or most comprehensive? Do they have a standard format? Do they complement each other? Overall, which database should be used for what purpose? If more than one database is deemed relevant, it is desirable to have a unified database containing information from all the shortlisted databases. There is no standard methodology yet for integrating biological pathway databases and, to the best of our knowledge, no commercial software that can perform such integration tasks. While XML based pathway data exchange standards such as BioPAX and SBML are emerging, these do not address the basic problems such as inconsistent nomenclature and substrate matching between databases in the unification of pathway databases. Here, we present the PathMeld methodology to unify KEGG and MetaCyc databases starting from their flat files. Individual PGDBs are transformed into a unified schema that we design. With individual PGDBs in the common unified schema, the key to the PathMeld methodology is to find the entity correspondences between the KEGG and MetaCyc substrates. We present a heuristic driven approach for one-to-one mapping of the substrates between KEGG and MetaCyc. Using the exact name and chemical formula match criteria, 82.6% of the substrates in MetaCyc were matched accurately to corresponding substrates in KEGG. The substrate names in the MetaCyc database contain html tags and non-characters such as <sub>, <sup>, <i>, <l>, &, and $. The MetaCyc chemical formula are stored in lisp format in the database while KEGG stores them as continuous strings. Hence, we subject MetaCyc chemical formulae to transformation into KEGG format to make them directly comparable. Applying pre-processing to transform MetaCyc substrate names and formulae improved substrate matching by 2%. To investigate how many of the remaining 17:4% substrates are indeed absent from KEGG, we employ a standard UNIX based approximate string matching tool called agrep. The resulting matches are curated into four mutually exlusive groups: 3:83% are correct matches, 3:17% are close matches, and 7:45% are incorrect matches. 3:68% of MetaCyc substrate names are not matched at all. This shows that 11:13% of MetaCyc substrate names are absent in KEGG. We note some of the implementation issues we solved. First, parsing only one flat file to populate one database table is not sufficient. Second, intermediate database tables are needed. Third, transformation of substrate names and chemical formula from one of the component databases is required for comparison. Fourth, a biochemist's intervention is needed in evaluating the approximate substrate matches from agrep. In conclusion, the PathMeld methodology successfully uni¯es KEGG and MetaCyc °at ¯le databases into a uni¯ed PostgreSQL database. Matching substrates between databases is the key issue in the uni¯cation process. About 83% of the substrate correspondences can be computationally achieved, while the remaining 17% substrates require approximate matching and manual curation by a biochemist. We presented several di®erent techniques for substrate matching and showed that about 10% of the MetaCyc substrates do not match and hence are absent from KEGG.<br>Master of Science
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Johnson, Stephen Robert. "iPathCase." Case Western Reserve University School of Graduate Studies / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=case1327935542.

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守屋, 勇樹. "ゲノムからのパスウェイ推定の為のバイオインフォマティクス研究". 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225302.

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Aloraini, Adel Abdullah M. "Extending the graphical representation of four KEGG pathways for a better understanding of prostate cancer using machine learning of graphical models." Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1711/.

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This thesis shows a novel contribution to computational biology alongside with developed machine learning methods. It shows how the graphical representation of KEGG pathways can be refined using machine learning of graphical models. The focus mainly is on a set of graphical models called Bayesian networks. Throughout this thesis , different ways of learning Bayesian networks are discussed. The work is based on Affymetrix gene expression microarray profiles and penalised Gaussian linear models. Penalisation in linear models includes choosing the most important parents and estimating the associated coefficients simultaneously using L1-regression. The sparse dataset that is generated from Affymetrix microarray technology is the key point in this thesis when learning Bayesian networks. Thus, the work in this thesis can be viewed as developing robust methods to avoid overfitting that usually associated with gene expression datasets and contributing to invoke more details about a well known discrepancy in KEGG pathways. So,the problem we have is to learn from a large number of candidates, small samples,(p>>n), and for such problem the goal is to apply model selection methods that hopefully achieve an accurate prediction , interpretable models, and stable models. The prediction and the most powerful predictors can be improved by using methods that trade-off between bias and variance. Also, providing which predictors are meaningful rather than using all predictors will provide interpretable models, and finally by choosing the most important predictors, a small change in the data will not result in large changes in the subset of predictors which consequently gives the stability to the models that are learnt.
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Csombordi, Rajmund. "Metabolomics database resolver." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417525.

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Metabolomics is a rising field combining bioinformatics and cheminformatics together. A major component of research is having a reliable data source, which usually comes in the form of metabolomic databases. This paper documents arising issues revolving categorizing metabolome compounds within databases, and a possible solution in the form of an R package that is capable of matching up various metabolome identifiers that originate from various metabolome databases. Then, by using this package we reflect on the average coverage of external reference between metabolome databases to highlight the lack of a universal compound primary identifier.<br><p>The thesis presentation was held over Zoom due to the recent COVID19 pandemic.</p>
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Ganesan, Sukirth M. "Relative Contributions Of Tobacco Associated Factors And Diabetes To Shaping The Oral Microbiome." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1529572658170786.

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Cheung, Ka-wing, and 張嘉穎. "Spatial and seasonal variations of freshwater macroinvertebrates, odonata and waterbirds in Luk Keng marshland, Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41290951.

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Tam, Tat-kuen. "Geology of Tiu Keng Leng new development area." View the Table of Contents & Abstract, 2001. http://sunzi.lib.hku.hk/hkuto/record/B30109243.

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Tam, Tat-kuen, and 譚達權. "Geology of Tiu Keng Leng new development area." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B4389463X.

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Isik, Zerrin, Tulin Ersahin, Volkan Atalay, Cevdet Aykanat, and Rengul Cetin-Atalay. "A signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq data." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-138982.

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Determination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts<br>Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich
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Libros sobre el tema "KEGG"

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Huo keng. Nei meng gu wen hua chu ban she, 1995.

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Tian keng. Bei fang wen yi chu ban she, 2009.

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Kōshi kego. Meiji Shoin, 1996.

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Beng, Ooi Kee. Keng qiang Jixiang. Yi teng yan jiu zhong xin, 2012.

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Poga, Laima. Kristaps Keggi: Intervijas. Jumava, 2000.

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Harte, Bryce. Powder keg. Berkley Books, 1991.

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Keng qi shi pu. Hua yuan wen hua shi ye you xian gong si, 2017.

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Keng qiang Qing Zang. Wen hua yi shu chu ban she, 2008.

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Pirie, Robert W. The kirkyards of Keig. Aberdeen & North-East Scotland Family History Society, 2001.

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Keng ru ping yi. Guangxi shi fan da xue chu ban she, 2013.

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Capítulos de libros sobre el tema "KEGG"

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Mehlhorn, Heinz. "KEGG." In Encyclopedia of Parasitology. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-43978-4_4597.

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Mehlhorn, Heinz. "KEGG." In Encyclopedia of Parasitology. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-27769-6_4597-1.

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Kanehisa, Minoru. "KEGG GLYCAN." In A Practical Guide to Using Glycomics Databases. Springer Japan, 2016. http://dx.doi.org/10.1007/978-4-431-56454-6_9.

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Qiu, Yu-Qing. "KEGG Pathway Database." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_472.

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Aoki-Kinoshita, Kiyoko F., and Minoru Kanehisa. "Glycomic Analysis Using KEGG GLYCAN." In Methods in Molecular Biology. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2343-4_7.

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Paul, Bini Elsa, Olaa Kasem, Haitao Zhao, and Zhong-Hui Duan. "Common Motifs in KEGG Cancer Pathways." In Advances in Computer Vision and Computational Biology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71051-4_60.

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Tokimatsu, Toshiaki, Masaaki Kotera, Susumu Goto, and Minoru Kanehisa. "KEGG and GenomeNet Resources for Predicting Protein Function from Omics Data Including KEGG PLANT Resource." In Protein Function Prediction for Omics Era. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0881-5_14.

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Kotera, Masaaki, Yuki Moriya, Toshiaki Tokimatsu, Minoru Kanehisa, and Susumu Goto. "KEGG and GenomeNet, New Developments, Metagenomic Analysis." In Encyclopedia of Metagenomics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7478-5_694.

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Kanehisa, Minoru. "Enzyme Annotation and Metabolic Reconstruction Using KEGG." In Methods in Molecular Biology. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7015-5_11.

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Zhou, Tingting. "Computational Reconstruction of Metabolic Networks from KEGG." In Methods in Molecular Biology. Humana Press, 2012. http://dx.doi.org/10.1007/978-1-62703-059-5_10.

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Actas de conferencias sobre el tema "KEGG"

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KANEHISA, MINORU. "KEGG FOR MEDICAL AND PHARMACEUTICAL APPLICATIONS." In The 6th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2007. http://dx.doi.org/10.1142/9781848161092_0002.

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Gerasch, Andreas, Michael Kaufmann, and Oliver Kohlbacher. "Rebuilding KEGG Maps: Algorithms and Benefits." In 2014 IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2014. http://dx.doi.org/10.1109/pacificvis.2014.45.

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Judeh, Thair, Tin Chi Nguyen, and Dongxiao Zhu. "QSEAfor fuzzy subgraph querying of KEGG pathways." In the ACM Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2382936.2382997.

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Ligtenberg, Willem P. A., Dragan Bosnacki, and Peter A. J. Hilbers. "Mining Maximal Frequent Subgraphs in KEGG Reaction Networks." In 2009 20th International Workshop on Database and Expert Systems Application. IEEE, 2009. http://dx.doi.org/10.1109/dexa.2009.66.

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Seipel, Dietmar. "Drug Design for KEGG Pathways with Answer Set Programming." In The International Symposium on Grids and Clouds (ISGC) 2012. Sissa Medialab, 2012. http://dx.doi.org/10.22323/1.153.0003.

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Zhou, Tingting, Samuel K. F. Yung, Zhenghua Wang, and Yunping Zhu. "A New Recursive Approach for Reconstructing Metabolic Networks from KEGG." In 2009 Fourth International Conference on Frontier of Computer Science and Technology (FCST). IEEE, 2009. http://dx.doi.org/10.1109/fcst.2009.28.

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D, Subhashini, and Daniel Alex Anand. "Network Biology of KEGG Enriched Viral Comorbidities in Psoriasis Subjects." In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT). IEEE, 2021. http://dx.doi.org/10.1109/i-pact52855.2021.9696642.

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Sultana, Kazi Zakia, Anupam Bhattacharjee, and Hasan Jamil. "IsoKEGG: A logic based system for querying biological pathways in KEGG." In 2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2010. http://dx.doi.org/10.1109/bibm.2010.5706642.

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Popescu, M., Dong Xu, and E. Taylor. "GoFuzzKegg: Mapping Genes to KEGG Pathways Using an Ontological Fuzzy Rule System." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221236.

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Liu, Chen-Lung, Chien-Ming Chen, Hui-Huang Hsu, and Tun-Wen Pai. "Discovering Feedback and Coupled Feedback Loops in KEGG Pathways through Cross-Map Identification." In 2013 7th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS). IEEE, 2013. http://dx.doi.org/10.1109/cisis.2013.102.

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