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Journal articles on the topic 'KEGG'

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Published: 4 June 2021
Last updated: 25 April 2022

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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

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

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3

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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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 (October 30, 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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5

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

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6

Hashimoto, Kosuke, Susumu Goto, Shin Kawano, Kiyoko F. Aoki-Kinoshita, Nobuhisa Ueda, Masami Hamajima, Toshisuke Kawasaki, and Minoru Kanehisa. "KEGG as a glycome informatics resource." Glycobiology 16, no. 5 (May 1, 2006): 63R—70R. http://dx.doi.org/10.1093/glycob/cwj010.

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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 (October 22, 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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8

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 (January 1, 1999): 29–34. http://dx.doi.org/10.1093/nar/27.1.29.

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9

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

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10

Kanehisa, M. "The KEGG resource for deciphering the genome." Nucleic Acids Research 32, no. 90001 (January 1, 2004): 277D—280. http://dx.doi.org/10.1093/nar/gkh063.

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11

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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12

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:10.5281/zenodo.7708.
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13

Klukas, C., and F. Schreiber. "Dynamic exploration and editing of KEGG pathway diagrams." Bioinformatics 23, no. 3 (December 1, 2006): 344–50. http://dx.doi.org/10.1093/bioinformatics/btl611.

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14

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 (October 13, 2018): D590—D595. http://dx.doi.org/10.1093/nar/gky962.

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15

Cui, Weiren, Lei Chen, Tao Huang, Qian Gao, Min Jiang, Ning Zhang, Lulu Zheng, Kaiyan Feng, Yudong Cai, and Hongwei Wang. "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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16

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 pathways created with CyKEGGParser makes systems biology analysis more sensitive and appropriate compared to original pathways.
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17

Mo, Sen, Chong Liu, Liyi Chen, Yuan Ma, Tuo Liang, Jiang Xue, HaoPeng Zeng, and Xinli Zhan. "KEGG-expressed genes and pathways in intervertebral disc degeneration." Medicine 98, no. 21 (May 2019): e15796. http://dx.doi.org/10.1097/md.0000000000015796.

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18

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, the enhanced tool enables loading experimental expression data into the network and simulating its dynamics under various biological conditions or perturbations. As a proof of concept, we tested two sets of published experimental data, one related to inflammatory bowel disease condition and the other to breast cancer treatment. We predict some of the major observations obtained following these laboratory experiments, and provide new insights that may shed additional light on these results. Tool requirements: Cytoscape 3.x, JAVA 8 Availability: The tool is freely available at http://bionsi.wix.com/bionsi, where a complete user guide and a step-by-step manual can also be found.
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19

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, the enhanced tool enables loading experimental expression data into the network and simulating its dynamics under various biological conditions or perturbations. As a proof of concept, we tested two sets of published experimental data, one related to inflammatory bowel disease condition and the other to breast cancer treatment. We predict some of the major observations obtained following these laboratory experiments, and provide new insights that may shed additional light on these results. Tool requirements: Cytoscape 3.x, JAVA 8 Availability: The tool is freely available at http://bionsi.wix.com/bionsi, where a complete user guide and a step-by-step manual can also be found.
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20

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

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21

Arakelyan, Arsen, and Lilit Nersisyan. "KEGGParser: parsing and editing KEGG pathway maps in Matlab." Bioinformatics 29, no. 4 (January 3, 2013): 518–19. http://dx.doi.org/10.1093/bioinformatics/bts730.

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22

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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23

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 (November 28, 2016): D353—D361. http://dx.doi.org/10.1093/nar/gkw1092.

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24

Kanehisa, M. "From genomics to chemical genomics: new developments in KEGG." Nucleic Acids Research 34, no. 90001 (January 1, 2006): D354—D357. http://dx.doi.org/10.1093/nar/gkj102.

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25

Kanehisa, M., M. Araki, S. Goto, M. Hattori, M. Hirakawa, M. Itoh, T. Katayama, et al. "KEGG for linking genomes to life and the environment." Nucleic Acids Research 36, Database (December 23, 2007): D480—D484. http://dx.doi.org/10.1093/nar/gkm882.

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26

Okuda, S., T. Yamada, M. Hamajima, M. Itoh, T. Katayama, P. Bork, S. Goto, and M. Kanehisa. "KEGG Atlas mapping for global analysis of metabolic pathways." Nucleic Acids Research 36, Web Server (May 19, 2008): W423—W426. http://dx.doi.org/10.1093/nar/gkn282.

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27

Liu, Yi, Bobin Mi, Huijuan Lv, Jing Liu, Yuan Xiong, Liangcong Hu, Hang Xue, Adriana C. Panayi, Guohui Liu, and Wu Zhou. "Shared KEGG pathways of icariin‐targeted genes and osteoarthritis." Journal of Cellular Biochemistry 120, no. 5 (December 3, 2018): 7741–50. http://dx.doi.org/10.1002/jcb.28048.

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28

Aramaki, Takuya, Romain Blanc-Mathieu, Hisashi Endo, Koichi Ohkubo, Minoru Kanehisa, Susumu Goto, and Hiroyuki Ogata. "KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold." Bioinformatics 36, no. 7 (November 19, 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://www.genome.jp/tools/kofamkoala/). Supplementary information Supplementary data are available at Bioinformatics online.
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29

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 (January 16, 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 database is freely accessible at http://www.insect-genome.com/miR-pathway. Researchers can intuitively determine the pathways and the genes in the pathways that are regulated by miRNAs as well as the miRNAs that target the pathways. To determine the pathways in which targets of a certain miRNA or multiple miRNAs are enriched, we performed a KEGG analysis miRNAs by using the hypergeometric test. In addition, miR+Pathway provides information regarding MTIs, PubMed IDs and the experimental verification method. Users can retrieve pathways regulated by an miRNA or a gene by inputting its names.
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30

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 represented on a KEGG pathway with all of the direct and indirect gene-gene interactions deduced from the pathway. KEGG2Net offers four different methods to remove cycles from the resulting gene interaction network, converting them into directed acyclic graphs (DAGs). We generated synthetic gene expression data using the gene interaction networks deduced from the KEGG pathways and performed a comparative analysis of different cycle removal methods by testing the fitness of their DAGs to the data and by the number of edges they eliminate. Our results indicate that an ensemble method for cycle removal performs as the best approach to convert the gene interaction networks into DAGs. Resulting gene interaction networks and DAGs are represented in multiple user-friendly formats that can be used in other applications, and as images for quick and easy visualisation. The KEGG2Net web portal converts KEGG maps for any organism into gene-gene interaction networks and corresponding DAGs representing all of the direct and indirect interactions among the genes.Availability: KEGG2Net is freely available at http://otulab.unl.edu/KEGG2Net
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31

Seo, Dongmin, Min-Ho Lee, and Seok Yu. "Development of Network Analysis and Visualization System for KEGG Pathways." Symmetry 7, no. 3 (July 16, 2015): 1275–88. http://dx.doi.org/10.3390/sym7031275.

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32

Wylie, Todd, John Martin, Sahar Abubucker, Yong Yin, David Messina, Zhengyuan Wang, James P. McCarter, and Makedonka Mitreva. "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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33

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 (October 17, 2015): D457—D462. http://dx.doi.org/10.1093/nar/gkv1070.

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34

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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35

Yu, Tao, Yuan Xiong, Simon Luu, Xiaomeng You, Bing Li, Jiang Xia, Hui Zhu, et al. "The shared KEGG pathways between icariin-targeted genes and osteoporosis." Aging 12, no. 9 (May 7, 2020): 8191–201. http://dx.doi.org/10.18632/aging.103133.

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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 (November 7, 2013): D199—D205. http://dx.doi.org/10.1093/nar/gkt1076.

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37

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

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38

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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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 terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.
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40

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 enrichment theory. The widely accepted feature selection method—maximum relevance and minimum redundancy (mRMR)—was applied to analyze and rank the features. An extensive literature review for the top 45 ranking features was conducted to confirm their close associations with CNV. Identifying the molecular biological mechanisms of CNV as described by the GO terms and KEGG pathways may contribute to improving the understanding of the pathogenesis of CNV.
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41

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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42

Richter, Stephan, Ingo Fetzer, Martin Thullner, Florian Centler, and Peter Dittrich. "Towards rule-based metabolic databases: a requirement analysis based on KEGG." International Journal of Data Mining and Bioinformatics 13, no. 3 (2015): 289. http://dx.doi.org/10.1504/ijdmb.2015.072103.

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43

Zhang, Jitao David, and Stefan Wiemann. "KEGGgraph: a graph approach to KEGG PATHWAY in R and bioconductor." Bioinformatics 25, no. 11 (March 23, 2009): 1470–71. http://dx.doi.org/10.1093/bioinformatics/btp167.

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44

Kanehisa, M., S. Goto, Y. Sato, M. Furumichi, and M. Tanabe. "KEGG for integration and interpretation of large-scale molecular data sets." Nucleic Acids Research 40, no. D1 (November 10, 2011): D109—D114. http://dx.doi.org/10.1093/nar/gkr988.

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45

Wrzodek, Clemens, Andreas Dräger, and Andreas Zell. "KEGGtranslator: visualizing and converting the KEGG PATHWAY database to various formats." Bioinformatics 27, no. 16 (June 23, 2011): 2314–15. http://dx.doi.org/10.1093/bioinformatics/btr377.

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46

Chen, Lei, Yu-Hang Zhang, Guohui Lu, Tao Huang, and Yu-Dong Cai. "Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways." Artificial Intelligence in Medicine 76 (February 2017): 27–36. http://dx.doi.org/10.1016/j.artmed.2017.02.001.

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47

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 experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.
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48

Zhang, Jian, ZhiHao Xing, Mingming Ma, Ning Wang, Yu-Dong Cai, Lei Chen, and Xun Xu. "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 of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.
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49

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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50

Liu, Kedi, Xingru Tao, Jing Su, Fei Li, Fei Mu, Shi Zhao, Xinming Lu, et al. "Network pharmacology and molecular docking reveal the effective substances and active mechanisms of Dalbergia Odoriferain protecting against ischemic stroke." PLOS ONE 16, no. 9 (September 28, 2021): e0255736. http://dx.doi.org/10.1371/journal.pone.0255736.

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Dalbergia Odorifera (DO) has been widely used for the treatment of cardiovascular and cerebrovascular diseasesinclinical. However, the effective substances and possible mechanisms of DO are still unclear. In this study, network pharmacology and molecular docking were used toelucidate the effective substances and active mechanisms of DO in treating ischemic stroke (IS). 544 DO-related targets from 29 bioactive components and 344 IS-related targets were collected, among them, 71 overlapping common targets were got. Enrichment analysis showed that 12 components were the possible bioactive components in DO, which regulating 9 important signaling pathways in 3 biological processes including ‘oxidative stress’ (KEGG:04151, KEGG:04068, KEGG:04915), ‘inflammatory response’(KEGG:04668, KEGG:04064) and ‘vascular endothelial function regulation’(KEGG:04066, KEGG:04370). Among these, 5 bioactive components with degree≥20 among the 12 potential bioactive components were selected to be docked with the top5 core targets using AutodockVina software. According to the results of molecular docking, the binding sites of core target protein AKT1 and MOL002974, MOL002975, and MOL002914 were 9, 8, and 6, respectively, and they contained 2, 1, and 0 threonine residues, respectively. And some binding sites were consistent, which may be the reason for the similarities and differences between the docking results of the 3 core bioactive components. The results of in vitro experiments showed that OGD/R could inhibit cell survival and AKT phosphorylation which were reversed by the 3 core bioactive components. Among them, MOL002974 (butein) had a slightly better effect. Therefore, the protective effect of MOL002974 (butein) against cerebral ischemia was further evaluated in a rat model of middle cerebral artery occlusion (MCAO) by detecting neurological score, cerebral infarction volume and lactate dehydrogenase (LDH) level. The results indicated that MOL002974 (butein) could significantly improve the neurological score of rats, decrease cerebral infarction volume, and inhibit the level of LDH in the cerebral tissue and serum in a dose-dependent manner. In conclusion, network pharmacology and molecular docking predicate the possible effective substances and mechanisms of DO in treating IS. And the results are verified by the in vitro and in vivo experiments. This research reveals the possible effective substances from DO and its active mechanisms for treating IS and provides a new direction for the secondary development of DO for treating IS.
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