Enrichment of metabolic routes through Big Data
The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway is a database that contains a graphical representation of cellular processes. Cellular processes are basic systems involving biochemical reactions at the cellular level such as transport, catabolism, metabolism, growth and cell death. The KE...
- Autores:
-
amelec, viloria
Torres, Marisela
Vargas, Jesus
Bonerge Pineda, Omar
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/6468
- Acceso en línea:
- https://hdl.handle.net/11323/6468
https://repositorio.cuc.edu.co/
- Palabra clave:
- Chemical-biological
Chemical compound
Data analytics
Metabolic pathways
Target fishing
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Enrichment of metabolic routes through Big Data |
| title |
Enrichment of metabolic routes through Big Data |
| spellingShingle |
Enrichment of metabolic routes through Big Data Chemical-biological Chemical compound Data analytics Metabolic pathways Target fishing |
| title_short |
Enrichment of metabolic routes through Big Data |
| title_full |
Enrichment of metabolic routes through Big Data |
| title_fullStr |
Enrichment of metabolic routes through Big Data |
| title_full_unstemmed |
Enrichment of metabolic routes through Big Data |
| title_sort |
Enrichment of metabolic routes through Big Data |
| dc.creator.fl_str_mv |
amelec, viloria Torres, Marisela Vargas, Jesus Bonerge Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Torres, Marisela Vargas, Jesus Bonerge Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Chemical-biological Chemical compound Data analytics Metabolic pathways Target fishing |
| topic |
Chemical-biological Chemical compound Data analytics Metabolic pathways Target fishing |
| description |
The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway is a database that contains a graphical representation of cellular processes. Cellular processes are basic systems involving biochemical reactions at the cellular level such as transport, catabolism, metabolism, growth and cell death. The KEGG Pathway information is shown through the use of graphs, in which the molecular interactions between genes, processes and chemical compounds are represented. This paper proposes to perform Data Analytics using the Big Data Analytics Life Cycle methodology to enrich the metabolic pathways of the KEGG Pathway database by applying the Target Fishing technique. |
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2020 |
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2020-07-06T20:18:49Z |
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2020-07-06T20:18:49Z |
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2020 |
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Artículo de revista |
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1877-0509 |
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https://hdl.handle.net/11323/6468 |
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10.1016/j.procs.2020.03.113 |
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Corporación Universidad de la Costa |
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eng |
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eng |
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[1] T. Erl, W. Khattak y P. Buhler, Big Data Fundamentals: Concepts, Drivers & Techniques, Indiana: Prentice Hall, 2016, p. 19. [2] J. D. J. Durán, F. Astier y S. Banov, «Bases de Datos vs Sistemas de Archivos,» 22 enero 2014. [En línea]. Available: https://prezi.com/jgrydc9ncude/bases-de-datos-vs-sistema-de- archivos/. [Último acceso: 12 Noviembre 2018]. [3] A. Sulaiman, «File System vs. Database, » 27 Abril 2017. [En línea]. Available: https://dzone.com/articles/which-is-better-saving-files-in- database-or-in-fil. [Último acceso: 12 Noviembre 2018]. [4] Fundamentos de Bases de Datos, «1.4 Sistemas de bases de datos frente a los sistemas de archivos,» mayo 2010. [En línea]. Available: https://fundamentosdebasededatos.files.wordpress.com/2010/05/equipo2.pdf. [Último acceso: 12 noviembre 2018]. [5] International Multimedia Resource Center, «RAM vs. Hard Drive Memory, » 2018. [En línea]. Available: https://www.lehigh.edu/~inimr/computer-basics- tutorial/ramvsdiskspacehtm.htm. [Último acceso: 13 noviembre 2018]. [6] Kanehisa Laboratories, «KEGG: Kyoto Encyclopedia of Genes and Genome, » 2018. [En línea]. Available: https://www.genome.jp/kegg/. [Último acceso: 25 07 2018]. [7] United States Environmental Protection Agency, Appendix F. SMILES Notation Tutorial, Washington D.C., 2017. [8] United States Environmental Protection Agency, «SMILES Tutorial,» 21 febrero 2016. [En línea]. Available: https://archive.epa.gov/med/med_archive_03/web/html/smiles.html. [Último acceso: 26 Julio 2018]. [9] Daylight Chemical Information Systems, «4. SMARTS - A Language for Describing Molecular Patterns, » 2008. [En línea]. Available: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. [Último acceso: 26 Julio 2018]. [10] TOCRIS, «Cell Biology,» 2018. [En línea]. Available: https://www.tocris.com/cell-biology. [Último acceso: 16 octubre 2018]. [11] Kyoto Encyclopedia of Genes and Genomes, «KEGG PATHWAY Database, » 21 Agosto 2018. [En línea]. Available: https://www.genome.jp/kegg/pathway.html. [Último acceso: 16 octubre 2018]. [12] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham. [13] Gamero, W. M., Ramírez, M. C., Parody, A., Viloria, A., López, M. H. A., & Kamatkar, S. J. (2018, June). Concentrations and size distributions of fungal bioaerosols in a municipal landfill. In International Conference on Data Mining and Big Data (pp. 244-253). Springer, Cham. [14] Kyoto Encyclopedia of Genes and Genomes, «KEGG release history, » 2018. [En línea]. Available: https://www.genome.jp/kegg/docs/upd_all.html. [Último acceso: 17 octubre 2018]. [15] M. Linderman, J. Sorenson, L. Lee y G. Nolan, «Computational solutions to large-scale data management and analysis, » Nature Reviews Genetics, vol. 11, pp. 647-657, 2010. [16] L. Wang y X. Qung Xie, «Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery? » Future Med Chem, vol. 6, nº 3, pp. 247-249, 2014 [17] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham. [18] J. Swamidass† y P. Baldi, «Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval, » Journal of Chemical Information and Modeling, vol. 47, nº 1, pp. 952-964, 2006. [19] S. Arif, J. Holliday y P. Willett, «Comparison of chemical similarity measures using different numbers of query structures, » Journal of Information Science, vol. 39, nº 1, pp. 1-8, 2013. [20] G. Landrum, «RDKit Documentation,» 01 marzo 2018. [En línea]. Available: https://www.rdkit.org/RDKit_Docs.current.pdf. [Último acceso: 10 septiembre 2018]. [21] L. Sánchez, «Distribución hipergeométrica de probabilidad,» 29 octubre 2014. [En línea]. Available: https://estadisticayadministracion.wordpress.com/2014/10/29/distribucion- hipergeometrica-de-probabilidad-cero-complicada/. [Último acceso: 16 Noviembre 2018]. [22] X. Su, «Introduction to Big Data, » 29 Agosto 2017. [En línea]. Available: https://www.ntnu.no/iie/fag/big/lessons/lesson2.pdf. [Último acceso: 16 enero 2018]. [23] K. Minoru y G. Susumu, «KEGG: Kyoto Encyclopedia of Genes and Genomes, » Nucleic Acids Research, vol. 28, nº 1, pp. 27-30, 2000. [24] The UniProt Consortium, «UniProt: the universal protein knowledgebase, » Nucleic Acids Research, vol. 45, nº 5, p. 2699, 2018. [25] The UniProt Consortium, «UniProt: the Universal Protein, » [En línea]. Available: https://www.uniprot.org/docs/uniprot_flyer.pdf. [Último acceso: 29 Julio 2018]. [26] A. Gaulton, L. Bellis, P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani y J. Overington, «ChEMBL: a large-scale bioactivity database for drug discovery, » Nucleic Acids Research, vol. 40, nº 1, pp. 1100-1107, 2012. [27] F. Haseltine, M. Huerta, Y. Liu, G. Downing y B. Seto, «NIH Working Definition of Bioinformatics and Computational Biology, » 17 Julio 2000. [En línea]. Available: http://www.bisti.nih.gov/docs/CompuBioDef.pdf. [Último acceso: 6 agosto 2018]. [28] M. Cruz Monteagudo, E. Tejera, Y. Pérez, J. Medina Fronco, A. Sánchez Rodríguez y F. Borges, «Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery, » Drug Discovery Today, vol. 22, nº 7, pp. 994-1007, 2017. [29] N. Wale y G. Karypis, «Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods, » Journal of Chemical Information and Modeling, vol. 49, nº 10, p. 2190–2201, 2009. [30] El Pasante, «Ventajas y desventajas de las bases de datos,» 17 junio 2015. [En línea]. Available: https://educacion.elpensante.com/ventajas- y-desventajas-de-las-bases-de- datos/. [Último acceso: 12 Noviembre 2018]. [31] Probability Formula, «Hypergeometric Distribution,» [En línea]. Available: http://www.probabilityformula.org/hypergeometric- distribution.html. [Último acceso: 16 noviembre 2018]. |
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amelec, viloriaTorres, MariselaVargas, JesusBonerge Pineda, Omar2020-07-06T20:18:49Z2020-07-06T20:18:49Z20201877-0509https://hdl.handle.net/11323/646810.1016/j.procs.2020.03.113Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway is a database that contains a graphical representation of cellular processes. Cellular processes are basic systems involving biochemical reactions at the cellular level such as transport, catabolism, metabolism, growth and cell death. The KEGG Pathway information is shown through the use of graphs, in which the molecular interactions between genes, processes and chemical compounds are represented. This paper proposes to perform Data Analytics using the Big Data Analytics Life Cycle methodology to enrich the metabolic pathways of the KEGG Pathway database by applying the Target Fishing technique.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Torres, MariselaVargas, JesusBonerge Pineda, OmarengProcedia Computer ScienceCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Chemical-biologicalChemical compoundData analyticsMetabolic pathwaysTarget fishingEnrichment of metabolic routes through Big DataArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] T. Erl, W. Khattak y P. Buhler, Big Data Fundamentals: Concepts, Drivers & Techniques, Indiana: Prentice Hall, 2016, p. 19.[2] J. D. J. Durán, F. Astier y S. Banov, «Bases de Datos vs Sistemas de Archivos,» 22 enero 2014. [En línea]. Available: https://prezi.com/jgrydc9ncude/bases-de-datos-vs-sistema-de- archivos/. [Último acceso: 12 Noviembre 2018].[3] A. Sulaiman, «File System vs. Database, » 27 Abril 2017. [En línea]. Available: https://dzone.com/articles/which-is-better-saving-files-in- database-or-in-fil. [Último acceso: 12 Noviembre 2018].[4] Fundamentos de Bases de Datos, «1.4 Sistemas de bases de datos frente a los sistemas de archivos,» mayo 2010. [En línea]. Available: https://fundamentosdebasededatos.files.wordpress.com/2010/05/equipo2.pdf. [Último acceso: 12 noviembre 2018].[5] International Multimedia Resource Center, «RAM vs. Hard Drive Memory, » 2018. [En línea]. Available: https://www.lehigh.edu/~inimr/computer-basics- tutorial/ramvsdiskspacehtm.htm. [Último acceso: 13 noviembre 2018].[6] Kanehisa Laboratories, «KEGG: Kyoto Encyclopedia of Genes and Genome, » 2018. [En línea]. Available: https://www.genome.jp/kegg/. [Último acceso: 25 07 2018].[7] United States Environmental Protection Agency, Appendix F. SMILES Notation Tutorial, Washington D.C., 2017.[8] United States Environmental Protection Agency, «SMILES Tutorial,» 21 febrero 2016. [En línea]. Available: https://archive.epa.gov/med/med_archive_03/web/html/smiles.html. [Último acceso: 26 Julio 2018].[9] Daylight Chemical Information Systems, «4. SMARTS - A Language for Describing Molecular Patterns, » 2008. [En línea]. Available: http://www.daylight.com/dayhtml/doc/theory/theory.smarts.html. [Último acceso: 26 Julio 2018].[10] TOCRIS, «Cell Biology,» 2018. [En línea]. Available: https://www.tocris.com/cell-biology. [Último acceso: 16 octubre 2018].[11] Kyoto Encyclopedia of Genes and Genomes, «KEGG PATHWAY Database, » 21 Agosto 2018. [En línea]. Available: https://www.genome.jp/kegg/pathway.html. [Último acceso: 16 octubre 2018].[12] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.[13] Gamero, W. M., Ramírez, M. C., Parody, A., Viloria, A., López, M. H. A., & Kamatkar, S. J. (2018, June). Concentrations and size distributions of fungal bioaerosols in a municipal landfill. In International Conference on Data Mining and Big Data (pp. 244-253). Springer, Cham.[14] Kyoto Encyclopedia of Genes and Genomes, «KEGG release history, » 2018. [En línea]. Available: https://www.genome.jp/kegg/docs/upd_all.html. [Último acceso: 17 octubre 2018].[15] M. Linderman, J. Sorenson, L. Lee y G. Nolan, «Computational solutions to large-scale data management and analysis, » Nature Reviews Genetics, vol. 11, pp. 647-657, 2010.[16] L. Wang y X. Qung Xie, «Computational target fishing: what should chemogenomics researchers expect for the future of in silico drug design and discovery? » Future Med Chem, vol. 6, nº 3, pp. 247-249, 2014[17] Viloria, A., Bucci, N., Luna, M., Lis-Gutiérrez, J. P., Parody, A., Bent, D. E. S., & López, L. A. B. (2018, June). Determination of dimensionality of the psychosocial risk assessment of internal, individual, double presence and external factors in work environments. In International Conference on Data Mining and Big Data (pp. 304-313). Springer, Cham.[18] J. Swamidass† y P. Baldi, «Mathematical Correction for Fingerprint Similarity Measures to Improve Chemical Retrieval, » Journal of Chemical Information and Modeling, vol. 47, nº 1, pp. 952-964, 2006.[19] S. Arif, J. Holliday y P. Willett, «Comparison of chemical similarity measures using different numbers of query structures, » Journal of Information Science, vol. 39, nº 1, pp. 1-8, 2013.[20] G. Landrum, «RDKit Documentation,» 01 marzo 2018. [En línea]. Available: https://www.rdkit.org/RDKit_Docs.current.pdf. [Último acceso: 10 septiembre 2018].[21] L. Sánchez, «Distribución hipergeométrica de probabilidad,» 29 octubre 2014. [En línea]. Available: https://estadisticayadministracion.wordpress.com/2014/10/29/distribucion- hipergeometrica-de-probabilidad-cero-complicada/. [Último acceso: 16 Noviembre 2018].[22] X. Su, «Introduction to Big Data, » 29 Agosto 2017. [En línea]. Available: https://www.ntnu.no/iie/fag/big/lessons/lesson2.pdf. [Último acceso: 16 enero 2018].[23] K. Minoru y G. Susumu, «KEGG: Kyoto Encyclopedia of Genes and Genomes, » Nucleic Acids Research, vol. 28, nº 1, pp. 27-30, 2000.[24] The UniProt Consortium, «UniProt: the universal protein knowledgebase, » Nucleic Acids Research, vol. 45, nº 5, p. 2699, 2018.[25] The UniProt Consortium, «UniProt: the Universal Protein, » [En línea]. Available: https://www.uniprot.org/docs/uniprot_flyer.pdf. [Último acceso: 29 Julio 2018].[26] A. Gaulton, L. Bellis, P. Bento, J. Chambers, M. Davies, A. Hersey, Y. Light, S. McGlinchey, D. Michalovich, B. Al-Lazikani y J. Overington, «ChEMBL: a large-scale bioactivity database for drug discovery, » Nucleic Acids Research, vol. 40, nº 1, pp. 1100-1107, 2012.[27] F. Haseltine, M. Huerta, Y. Liu, G. Downing y B. Seto, «NIH Working Definition of Bioinformatics and Computational Biology, » 17 Julio 2000. [En línea]. Available: http://www.bisti.nih.gov/docs/CompuBioDef.pdf. [Último acceso: 6 agosto 2018].[28] M. Cruz Monteagudo, E. Tejera, Y. Pérez, J. Medina Fronco, A. Sánchez Rodríguez y F. Borges, «Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery, » Drug Discovery Today, vol. 22, nº 7, pp. 994-1007, 2017.[29] N. Wale y G. Karypis, «Target Fishing for Chemical Compounds Using Target-Ligand Activity Data and Ranking Based Methods, » Journal of Chemical Information and Modeling, vol. 49, nº 10, p. 2190–2201, 2009.[30] El Pasante, «Ventajas y desventajas de las bases de datos,» 17 junio 2015. [En línea]. Available: https://educacion.elpensante.com/ventajas- y-desventajas-de-las-bases-de- datos/. [Último acceso: 12 Noviembre 2018].[31] Probability Formula, «Hypergeometric Distribution,» [En línea]. Available: http://www.probabilityformula.org/hypergeometric- distribution.html. 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