Comparación de la representación multiplex y monoplex de redes de co-expresión génica

ilustraciones, gráficas, tablas

Autores:: Salas Cárdenas, Yesica Alejandra

Tipo de recurso:

Fecha de publicación:: 2020

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

id	UNACIONAL2_7063c864ba046639fe28bd47ae297893
oai_identifier_str	oai:repositorio.unal.edu.co:unal/80644
network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Comparación de la representación multiplex y monoplex de redes de co-expresión génica
dc.title.translated.eng.fl_str_mv	Comparison of multiplex and monoplex representation of gene co-expression networks
title	Comparación de la representación multiplex y monoplex de redes de co-expresión génica
spellingShingle	Comparación de la representación multiplex y monoplex de redes de co-expresión génica 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Biostatistics Genomics Escherichia coli Bioestadística Genómica Escherichia coli Red multiplex Red monoplex Red agregada Algoritmo de alineación Medidas de centralidad Multiplex network Monoplex network E.coli Aggregated network Alignment algorithm Centrality measure
title_short	Comparación de la representación multiplex y monoplex de redes de co-expresión génica
title_full	Comparación de la representación multiplex y monoplex de redes de co-expresión génica
title_fullStr	Comparación de la representación multiplex y monoplex de redes de co-expresión génica
title_full_unstemmed	Comparación de la representación multiplex y monoplex de redes de co-expresión génica
title_sort	Comparación de la representación multiplex y monoplex de redes de co-expresión génica
dc.creator.fl_str_mv	Salas Cárdenas, Yesica Alejandra
dc.contributor.advisor.spa.fl_str_mv	Lopez-Kleine, Liliana
dc.contributor.author.spa.fl_str_mv	Salas Cárdenas, Yesica Alejandra
dc.contributor.researchgroup.spa.fl_str_mv	METODOS EN BIOESTADISTICA
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Biostatistics Genomics Escherichia coli Bioestadística Genómica Escherichia coli Red multiplex Red monoplex Red agregada Algoritmo de alineación Medidas de centralidad Multiplex network Monoplex network E.coli Aggregated network Alignment algorithm Centrality measure
dc.subject.decs.eng.fl_str_mv	Biostatistics Genomics Escherichia coli
dc.subject.decs.spa.fl_str_mv	Bioestadística Genómica Escherichia coli
dc.subject.proposal.spa.fl_str_mv	Red multiplex Red monoplex Red agregada Algoritmo de alineación Medidas de centralidad
dc.subject.proposal.eng.fl_str_mv	Multiplex network Monoplex network E.coli Aggregated network Alignment algorithm Centrality measure
description	ilustraciones, gráficas, tablas
publishDate	2020
dc.date.issued.none.fl_str_mv	2020-12-01
dc.date.accessioned.none.fl_str_mv	2021-11-02T18:07:04Z
dc.date.available.none.fl_str_mv	2021-11-02T18:07:04Z
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/80644
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/80644 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	Aldana, M. (2011). Redes complejas: Estructura, dinámica y evolución (1.a ed.). Aleta, A., y Moreno, Y. (2018). Multilayer networks in a nutshell. CoRR. Alonso, J., y Carabalí, J. (2019, 04). Breve tutorial para visualizar y calcular métricas de redes (grafos) en r (para economistas). , 7. Battiston, F., Nicosia, V., y Latora, V. (2016, 06). The new challenges of multiplex networks: Measures and models. The European Physical Journal Special Topics, 226 . doi: 10.1140/epjst/e2016-60274-8. Berlingerio, M., Coscia, M., y Giannotti, F. (2011). Finding and characterizing communities in multidimensional networks. 2011 International Conference on Advances in Social Networks Analysis and Mining, 490-494. Bianconi, G. (2018). Multilayer networks : structure and function (1.a ed.). Oxford University Press. Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., Gómez-Gardeñes, J., Romance, M., Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544 , 1 - 122. Descargado de http://www .sciencedirect.com/science/article/pii/S0370157314002105 doi: https://doi.org/10.1016/j.physrep.2014.07.001. Cai, D., Shao, Z., He, X., Yan, X., y Han, J. (2005). Mining hidden community in heterogeneous social networks. En Proceedings of the 3rd international workshop on link discovery (p. 58-65). New York, NY, USA: Association for Computing Machinery. Descargado de https://doi.org/10.1145/1134271.1134280 doi: 10.1145/1134271.1134280. Cardillo, A., Zanin, M., Gómez-Garde~nes, J., Romance, M., Garc a del Amo, A., y Boccaletti, S. (2013, 01). Modeling the multi-layer nature of the european air transport network: Resilience and passengers re-scheduling under random failures. The European Physical Journal Special Topics, 215 , 23-33. doi: 10.1140/epjst/e2013-01712-8. Covert, M., Knight, E., Reed, J., Herrgard, M., y Palsson, B. (2004, 06). Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429 , 92-6. doi: 10.1038/nature02456. Csardi, G., y Nepusz, T. (2005, 11). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. Dalgaard, P. (2008). Introductory statistics with r (2.a ed.). Dam, S., Vosa, U., Graaf, A., Franke, L., y de Magalhaes, J. P. (2017, 01). Gene co-expression analysis for functional classification and gene-disease predictions. Briefings in bioinformatics, 19 . doi: 10.1093/bib/bbw139. De Domenico, M. (2017). Multilayer modeling and analysis of human brain networks. GigaScience, 6 (5), gix004. Descargado de http://dx.doi.org/ 10.1093/gigascience/gix004 doi: 10.1093/gigascience/gix004. De Domenico, M., Porter, M. A., y Arenas, A. (2014, 10). MuxViz: a tool for multilayer analysis and visualization of networks. Journal of Complex Networks, 3 (2), 159-176. Descargado de https://doi.org/10.1093/comnet/cnu038 doi: 10.1093/comnet/cnu038. D'haeseleer, P., Liang, S., y Somogyi, R. (2000, 09). Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics (Oxford, England), 16 , 707-26. doi: 10.1093/bioinformatics/16.8.707. Dickison, M., Havlin, S., y Stanley, H. E. (2012, Jun). Epidemics on interconnected networks. Phys. Rev. E, 85 , 066109. Descargado de https://link.aps.org/ doi/10.1103/PhysRevE.85.066109 doi: 10.1103/PhysRevE.85.066109. Didier, G., Brun, C., y Baudot, A. (2015). Identifying communities from multiplex biological networks. PeerJ , 3 , e1525. Descargado de https://doi.org/10.7717/peerj.1525 doi: 10.7717/peerj.1525. Domenico, M. D. (2018). Multilayer network modeling of integrated biological systems: Comment on \network science of biological systems at different scales: A review" by gosak et al. Physics of Life Reviews, 24 , 149 - 152. Descargado de http://www.sciencedirect./science/article/pii/ S1571064517301926 doi: https://doi.org/10.1016/j.plrev.2017.12.006. Elena, P. Q. M. (2018). Evaluación de métodos de comparación de redes biológicas y su capacidad para detectar estructuras similares. Universidad Nacional de Colombia, Sede-Bogotá. Elo, L. L., Järvenpää, H., Oresic, M., Lahesmaa, R., y Aittokallio, T. (2007, 06). Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics, 23 (16), 2096- 2103. Descargado de https://doi.org/10.1093/bioinformatics/btm309 doi: 10.1093/bioinformatics/btm309. Fong, S., Burgard, A., Herring, C., Knight, E. M., Blattner, F., Maranas, C., y Palsson, B. (2005). In silico design and adaptive evolution of escherichia coli for production of lactic acid. Biotechnology and bioengineering, 91 5 , 643-8. Fong, S., Joyce, A., y Palsson, B. (2005). Parallel adaptive evolution cultures of escherichia coli lead to convergent growth phenotypes with di erent gene expression states. Genome research, 15 10 , 1365-72. Fong, S., Nanchen, A., Palsson, B., y Sauer, U. (2006, 04). Latent pathway activation and increased pathway capacity enable escherichia coli adaptation to loss of key metabolic enzymes. The Journal of biological chemistry, 281, 8024-33. doi: 10.1074/jbc.M510016200. Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Zhang, J. (2004, 02). Bioconductor: Open software development for computational biology and bioinformatics. Genome biology, 5 , R80. doi: 10.1186/gb-2004-5-10-r80. González Prieto, C. A. (2018). Construcción de redes de regulación génica usando datos de secuenciaci on de arn. Universidad Nacional de Colombia, Sede-Bogotá. Gosak, M., Markovic, R., Dolensek, J., Rupnik, M. S., Marhl, M., Stozer, A., y Perc, M. (2018). Network science of biological systems at different scales: A review. Physics of Life Reviews, 24 , 118 - 135. Descargado de http://www .sciencedirect.com/science/article/pii/S1571064517301501 doi: https://doi.org/10.1016/j.plrev.2017.11.003. Herrgard, M. J., Fong, S., y Palsson, B. (2006). Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Computational Biology, 2. Holme, P., y Saram aki, J. (2012). Temporal networks. Physics Reports, 519 (3), 97 - 125. Descargado de http://www.sciencedirect.com/science/article/ pii/S0370157312000841 (Temporal Networks) doi: https://doi.org/10 .1016/j.physrep.2012.03.001. Hua, Q., Joyce, A. R., Fong, S. S., y Palsson, B. (2006). Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains. Bio- technology and Bioengineering, 95 (5), 992-1002. Descargado de https:// onlinelibrary.wiley.com/doi/abs/10.1002/bit.21073 doi: https:// doi.org/10.1002/bit.21073. Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., y Vingron, M. (2002, 07). Variance stabilization applied to microarray data calibration and to the quantification of di erential expression. Bioinformatics, 18 , S96-S104. Descargado de https://doi.org/10.1093/bioinformatics/18.suppl/_1.S96 doi: 10.1093/bioinformatics/18.suppl_1.S96. Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K. J., Scherf, U., y Speed, T. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4 2 , 249-64. Jalili, M., Salehzadeh-Yazdi, A., Asgari, Y., Arab, S. S., Yaghmaie, M., Ardeshir, G., y Alimoghaddam, K. (2015, 11). Centiserver: A comprehensive resource, web-based application and r package for centrality analysis. PloS one, 10 , e0143111. doi: 10.1371/journal.pone.0143111. Kaluza, P., Kölzsch, A., Gastner, M. T., y Blasius, B. (2010). The complex network of global cargo ship movements. Journal of the Royal Society, Interface, 1093-103. Kanawati, R. (2015). Multiplex network mining: A brief survey. IEEE Intell. Informatics Bull., 16 , 24-27. Kim, H., Shim, J. E., Shin, J., y Lee, I. (2015, 02). EcoliNet: a database of cofunctional gene network for Escherichia coli. Database, 2015 . Descargado de https://doi.org/10.1093/database/bav001 (bav001) doi: 10.1093/ database/bav001. Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., y Porter, M. A. (2014). Multilayer networks. J. Complex Networks, 2 , 203-271. Kuchaiev, O., y Przulj, N. (2011, May). Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics (Oxford, England), 27 (10), 1390-1396. Descargado de https://doi.org/ 10.1093/bioinformatics/btr127 doi: 10.1093/bioinformatics/btr127. Leal Alaya, L. G. (2013). Desarrollo de una metodología estadística aplicada a la construcción y comparación de redes de coexpresión génica. Universidad Nacional de Colombia, Sede-Bogotá. Leandro, G. T. (2018). Medidas de centralidad en redesurbanas con datos . Universidad de Alicante, España. Lewis, N. E., Cho, B.-K., Knight, E. M., y Palsson, B. O. (2009). Gene expression profiling and the use of genome-scale in silico models of escherichia coli for analysis: Providing context for content. Journal of Bacteriology, 191 (11), 3437-3444. Descargado de https://jb.asm.org/content/191/11/3437 doi: 10.1128/JB.00034-09. Leybovich, I., Puzis, R., Stern, R., y Reuben, M. (2018). Focused sana: Speeding up network alignment. En Socs. Li, W., Dai, C., Zhou, X. J., Tseng, G., Ghosh, D., y Zhou, X. J. (2015). Integrative analysis of many biological networks to study gene regulation. , 68-87. doi: 10.1017/CBO9781107706484.004. Li, W., Liu, C.-C., Zhang, T., Li, H., Waterman, M. S., y Zhou, X. J. (2011). Integrative analysis of many weighted co-expression networks using tensor computation. Lotero-Vélez, L., y Hurtado, R. (2014). Vulnerabilidad de redes complejas y aplicaciones al transporte urbano: Una revisi on de la literatura. Revista EIA, 11 , 67. Louzada, V., Ara ujo, N., Andrade, J. S., y Herrmann, H. (2013). Breathing synchronization in interconnected networks. Scientific Reports, 3. Mamano, N., y Hayes, W. B. (2017, 02). SANA: simulated annealing far outperforms many other search algorithms for biological network alignment. Bioinformatics, 33 (14), 2156-2164. Descargado de https://doi.org/10.1093/bioinformatics/btx090 doi: 10.1093/bioinformatics/btx090. Michael, C. (2018). Implementación del método de alineamiento de redesgedevo en R. Universidad Nacional de Colombia, Sede-Bogotá. Michoel, T., y Nachtergaele, B. (2012). Alignment and integration of complex networks by hypergraph-based spectral clustering. Physical review. E, Statistical, nonlinear, and soft matter physics, 056111. Mutwil, M., Klie, S., Tohge, T., Giorgi, F., Wilkins, O., Campbell, M., Persson, S. (2011, 03). Planet: Combined sequence and expression comparisons across plant networks derived from seven species. The Plant cell , 23 , 895-910. doi:10.1105/tpc.111.083667 Newman, E. J. (2010). Networks: An introduction (1.a ed.). Oxford University Press. Newman, M. (2002, 12). Assortative mixing in networks. Physical review letters, 89 , 208701. doi: 10.1103/PhysRevLett.89.208701. Otte, E., y Rousseau, R. (2002, 12). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28 , 441-453. doi: 10.1177/016555150202800601. Perkins, A., y Langston, M. (2009, 10). Threshold selection in gene co-expression networks using spectral graph theory techniques. BMC bioinformatics, 10 Suppl 11 , S4. doi: 10.1186/1471-2105-10-S11-S4. Porter, M. (2018). What is a multilayer network. Notices of the American Mathematical Society, 65 , 1419-1423. Reed, J., Famili, I., Thiele, I., y Palsson, B. (2006). Towards multidimensional genome annotation. Nature Reviews Genetics, 7 , 130-141. Singh, V., Rana, R., y Singhal, R. (2013, 04). Analysis of repeated measurement data in the clinical trials. Journal of Ayurveda and integrative medicine, 4 , 77-81. doi: 10.4103/0975-9476.113872. Soranzo, N., Bianconi, G., y Altafini, C. (2007a, 05). Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics, 23 (13), 1640-1647. Descargado de https://doi.org/10.1093/bioinformatics/btm163 doi: 10.1093/bioinformatics/btm163. Soranzo, N., Bianconi, G., y Altafini, C. (2007b, 05). Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics, 23 (13), 1640-1647. Descargado de https://doi.org/10.1093/bioinformatics/btm163 doi: 10.1093/bioinformatics/btm163. Welsh, E., Eschrich, S., Berglund, A., y Fenstermacher, D. (2013, 05). Iterative rankorder normalization of gene expression microarray data. BMC bioinformatics, 14 , 153. doi: 10.1186/1471-2105-14-153. YANG, S. (2013). Networks: An introduction by m. e. j. newman. The Journal of Mathematical Sociology, 37 (4), 250-251. Descargado de https://doi.org/ 10.1080/0022250X.2012.744247 doi: 10.1080/0022250X.2012.744247. Zanin, M., y Lillo, F. (2013). Modelling the air transport with complex networks: A short review. The European Physical Journal Special Topics, 215 , 5-21.
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv	Atribución-NoComercial 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv	openAccess
dc.format.extent.spa.fl_str_mv	xi, 50 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.department.spa.fl_str_mv	Departamento de Estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
bitstream.url.fl_str_mv	https://repositorio.unal.edu.co/bitstream/unal/80644/6/1032450483.2020.pdf https://repositorio.unal.edu.co/bitstream/unal/80644/3/license.txt https://repositorio.unal.edu.co/bitstream/unal/80644/7/1032450483.2020.pdf.jpg
bitstream.checksum.fl_str_mv	a48989eaa62964b7ab7c7766f182ba07 8153f7789df02f0a4c9e079953658ab2 6e6512559b14382d4c0f30572068c5c0
bitstream.checksumAlgorithm.fl_str_mv	MD5 MD5 MD5
repository.name.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv	repositorio_nal@unal.edu.co
_version_	1814089683992313856
spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lopez-Kleine, Liliana0545b3e38bb78f7d904599de9c18f3d5600Salas Cárdenas, Yesica Alejandraf758a3a1610499eff80eaab2acf7d317METODOS EN BIOESTADISTICA2021-11-02T18:07:04Z2021-11-02T18:07:04Z2020-12-01https://repositorio.unal.edu.co/handle/unal/80644Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficas, tablasLa teoría de redes ha permitido caracterizar el comportamiento de un sistema en diferentes ámbitos, como es el caso del estudio de los sistemas complejos a través de la representación de redes de una capa (monoplex), las cuales estudian las relaciones subyacentes entre los nodos de un sistema. Recientemente, la teoría de redes ha evolucionado desarrollando el estudio de redes multicapa con el objetivo de incluir múltiples relaciones y representar las relaciones intra y entre capa, donde las intra son consideradas como en el caso monoplex. En el campo de genómica, aunque se han abordado algunas metodologías de redes multicapa, no se ha enfatizado en el caso de RCG, ni se han hecho comparaciones con la metodología tradicional de redes monoplex que ha sido utilizada hasta la fecha. El presente trabajo adoptó una metodología de redes multiplex, considerando nodos réplica (genes) en las capas y cuyas interacciones inter-capa es vacío. Las capas son RCG que corresponden a múltiples condiciones experimentales de la E. coli y cuya colección forman la estructura multiplex. El enfoque de RCG multiplex permitió hacer un aplanamiento de la estructura multiplex, en una sóla red agregada. Se buscó caracterizar y evaluar la representación de la red de co-expresión génica de la E. coli, comparando la representación monoplex frente a la multiplex, utilizando la red agregada, a través de sus medidas topológicas, propiedades globales y locales, medidas de centralidad, matriz de distancia, anovas, pruebas pareadas-t y algoritmos de alineamiento de redes, que permitieron evaluar las diferencias, y similitudes de la información obtenida de cada representación monoplex y multiplex con respecto a la red de referencia. Se sugieren avances y mejoras en el estudio de las RCG, ya que la red agregada proveniente de la estructura multiplex, estructuralmente se asemeja más a la red de referencia de la E. coli, mientras que la red monoplex precisa mayor pérdida de información que la red agregada, al compararlas con la red de referencia. (Texto tomado de la fuente).Network theory has allowed us to characterize the behavior of a system in different areas, such as the study of complex systems through the representation of single-layer networks (monoplex), which study the relationships between the nodes of a system. Recently, network theory has evolved developing the study of multi-layer networks with the aim of including multiple relationships and representing the intra and inter-layer relationships, like single-layer networks case. In the genomics fi eld, although some multilayer network methodologies have been addressed, but not all of them have been developed on the RCG, besides no comparisons have been made with the traditional monoplex network methodology that has been used to date. This study is based on a multiplex network methodology, considering nodes (genes) replicated in the layers and whose set of interactions between layers is empty. The layers are RCG that correspond to multiple experimental conditions of E. coli and whose collection forms the multiplex structure. The multiplex RCG approach allowed to do a attening in a single aggregated network. The aim was to characterize and evaluate the representation of the E. coli gene coexpression network, comparing the monoplex representation against the multiplex representation, using the aggregate network, its topological measures, global and local properties, centrality measures, matrix of distance, anova, paired t-tests and network alignment algorithms, which allowed evaluating the differences and similarities of the information obtained from each monoplex and multiplex representation with respect to the reference network. This project suggested advances and improvements in the study of RCG, because the aggregated network coming from the multiplex structure, is more similar structurally to the reference network of the 'E. coli', while the monoplex network shows a greater loss of information than the aggregated network, when those are compared with the reference networkMaestríaMagíster en Ciencias - EstadísticaEstadística genómicaxi, 50 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasBiostatisticsGenomicsEscherichia coliBioestadísticaGenómicaEscherichia coliRed multiplexRed monoplexRed agregadaAlgoritmo de alineaciónMedidas de centralidadMultiplex networkMonoplex networkE.coliAggregated networkAlignment algorithmCentrality measureComparación de la representación multiplex y monoplex de redes de co-expresión génicaComparison of multiplex and monoplex representation of gene co-expression networksTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAldana, M. (2011). Redes complejas: Estructura, dinámica y evolución (1.a ed.).Aleta, A., y Moreno, Y. (2018). Multilayer networks in a nutshell. CoRR.Alonso, J., y Carabalí, J. (2019, 04). Breve tutorial para visualizar y calcular métricas de redes (grafos) en r (para economistas). , 7.Battiston, F., Nicosia, V., y Latora, V. (2016, 06). The new challenges of multiplex networks: Measures and models. The European Physical Journal Special Topics, 226 . doi: 10.1140/epjst/e2016-60274-8.Berlingerio, M., Coscia, M., y Giannotti, F. (2011). Finding and characterizing communities in multidimensional networks. 2011 International Conference on Advances in Social Networks Analysis and Mining, 490-494.Bianconi, G. (2018). Multilayer networks : structure and function (1.a ed.). Oxford University Press.Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., Gómez-Gardeñes, J., Romance, M., Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544 , 1 - 122. Descargado de http://www .sciencedirect.com/science/article/pii/S0370157314002105 doi: https://doi.org/10.1016/j.physrep.2014.07.001.Cai, D., Shao, Z., He, X., Yan, X., y Han, J. (2005). Mining hidden community in heterogeneous social networks. En Proceedings of the 3rd international workshop on link discovery (p. 58-65). New York, NY, USA: Association for Computing Machinery. Descargado de https://doi.org/10.1145/1134271.1134280 doi: 10.1145/1134271.1134280.Cardillo, A., Zanin, M., Gómez-Garde~nes, J., Romance, M., Garc a del Amo, A., y Boccaletti, S. (2013, 01). Modeling the multi-layer nature of the european air transport network: Resilience and passengers re-scheduling under random failures. The European Physical Journal Special Topics, 215 , 23-33. doi: 10.1140/epjst/e2013-01712-8.Covert, M., Knight, E., Reed, J., Herrgard, M., y Palsson, B. (2004, 06). Integrating high-throughput and computational data elucidates bacterial networks. Nature, 429 , 92-6. doi: 10.1038/nature02456.Csardi, G., y Nepusz, T. (2005, 11). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.Dalgaard, P. (2008). Introductory statistics with r (2.a ed.).Dam, S., Vosa, U., Graaf, A., Franke, L., y de Magalhaes, J. P. (2017, 01). Gene co-expression analysis for functional classification and gene-disease predictions. Briefings in bioinformatics, 19 . doi: 10.1093/bib/bbw139.De Domenico, M. (2017). Multilayer modeling and analysis of human brain networks. GigaScience, 6 (5), gix004. Descargado de http://dx.doi.org/ 10.1093/gigascience/gix004 doi: 10.1093/gigascience/gix004.De Domenico, M., Porter, M. A., y Arenas, A. (2014, 10). MuxViz: a tool for multilayer analysis and visualization of networks. Journal of Complex Networks, 3 (2), 159-176. Descargado de https://doi.org/10.1093/comnet/cnu038 doi: 10.1093/comnet/cnu038.D'haeseleer, P., Liang, S., y Somogyi, R. (2000, 09). Genetic network inference: From co-expression clustering to reverse engineering. Bioinformatics (Oxford, England), 16 , 707-26. doi: 10.1093/bioinformatics/16.8.707.Dickison, M., Havlin, S., y Stanley, H. E. (2012, Jun). Epidemics on interconnected networks. Phys. Rev. E, 85 , 066109. Descargado de https://link.aps.org/ doi/10.1103/PhysRevE.85.066109 doi: 10.1103/PhysRevE.85.066109.Didier, G., Brun, C., y Baudot, A. (2015). Identifying communities from multiplex biological networks. PeerJ , 3 , e1525. Descargado de https://doi.org/10.7717/peerj.1525 doi: 10.7717/peerj.1525.Domenico, M. D. (2018). Multilayer network modeling of integrated biological systems: Comment on \network science of biological systems at different scales: A review" by gosak et al. Physics of Life Reviews, 24 , 149 - 152. Descargado de http://www.sciencedirect./science/article/pii/ S1571064517301926 doi: https://doi.org/10.1016/j.plrev.2017.12.006.Elena, P. Q. M. (2018). Evaluación de métodos de comparación de redes biológicas y su capacidad para detectar estructuras similares. Universidad Nacional de Colombia, Sede-Bogotá.Elo, L. L., Järvenpää, H., Oresic, M., Lahesmaa, R., y Aittokallio, T. (2007, 06). Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process. Bioinformatics, 23 (16), 2096- 2103. Descargado de https://doi.org/10.1093/bioinformatics/btm309 doi: 10.1093/bioinformatics/btm309.Fong, S., Burgard, A., Herring, C., Knight, E. M., Blattner, F., Maranas, C., y Palsson, B. (2005). In silico design and adaptive evolution of escherichia coli for production of lactic acid. Biotechnology and bioengineering, 91 5 , 643-8.Fong, S., Joyce, A., y Palsson, B. (2005). Parallel adaptive evolution cultures of escherichia coli lead to convergent growth phenotypes with di erent gene expression states. Genome research, 15 10 , 1365-72.Fong, S., Nanchen, A., Palsson, B., y Sauer, U. (2006, 04). Latent pathway activation and increased pathway capacity enable escherichia coli adaptation to loss of key metabolic enzymes. The Journal of biological chemistry, 281, 8024-33. doi: 10.1074/jbc.M510016200.Gentleman, R., Carey, V., Bates, D., Bolstad, B., Dettling, M., Dudoit, S., Zhang, J. (2004, 02). Bioconductor: Open software development for computational biology and bioinformatics. Genome biology, 5 , R80. doi: 10.1186/gb-2004-5-10-r80.González Prieto, C. A. (2018). Construcción de redes de regulación génica usando datos de secuenciaci on de arn. Universidad Nacional de Colombia, Sede-Bogotá.Gosak, M., Markovic, R., Dolensek, J., Rupnik, M. S., Marhl, M., Stozer, A., y Perc, M. (2018). Network science of biological systems at different scales: A review. Physics of Life Reviews, 24 , 118 - 135. Descargado de http://www .sciencedirect.com/science/article/pii/S1571064517301501 doi: https://doi.org/10.1016/j.plrev.2017.11.003.Herrgard, M. J., Fong, S., y Palsson, B. (2006). Identification of genome-scale metabolic network models using experimentally measured flux profiles. PLoS Computational Biology, 2.Holme, P., y Saram aki, J. (2012). Temporal networks. Physics Reports, 519 (3), 97 - 125. Descargado de http://www.sciencedirect.com/science/article/ pii/S0370157312000841 (Temporal Networks) doi: https://doi.org/10 .1016/j.physrep.2012.03.001.Hua, Q., Joyce, A. R., Fong, S. S., y Palsson, B. (2006). Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains. Bio- technology and Bioengineering, 95 (5), 992-1002. Descargado de https:// onlinelibrary.wiley.com/doi/abs/10.1002/bit.21073 doi: https:// doi.org/10.1002/bit.21073.Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., y Vingron, M. (2002, 07). Variance stabilization applied to microarray data calibration and to the quantification of di erential expression. Bioinformatics, 18 , S96-S104. Descargado de https://doi.org/10.1093/bioinformatics/18.suppl/_1.S96 doi: 10.1093/bioinformatics/18.suppl_1.S96.Irizarry, R., Hobbs, B., Collin, F., Beazer-Barclay, Y., Antonellis, K. J., Scherf, U., y Speed, T. (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4 2 , 249-64.Jalili, M., Salehzadeh-Yazdi, A., Asgari, Y., Arab, S. S., Yaghmaie, M., Ardeshir, G., y Alimoghaddam, K. (2015, 11). Centiserver: A comprehensive resource, web-based application and r package for centrality analysis. PloS one, 10 , e0143111. doi: 10.1371/journal.pone.0143111.Kaluza, P., Kölzsch, A., Gastner, M. T., y Blasius, B. (2010). The complex network of global cargo ship movements. Journal of the Royal Society, Interface, 1093-103.Kanawati, R. (2015). Multiplex network mining: A brief survey. IEEE Intell. Informatics Bull., 16 , 24-27.Kim, H., Shim, J. E., Shin, J., y Lee, I. (2015, 02). EcoliNet: a database of cofunctional gene network for Escherichia coli. Database, 2015 . Descargado de https://doi.org/10.1093/database/bav001 (bav001) doi: 10.1093/ database/bav001.Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., y Porter, M. A. (2014). Multilayer networks. J. Complex Networks, 2 , 203-271.Kuchaiev, O., y Przulj, N. (2011, May). Integrative network alignment reveals large regions of global network similarity in yeast and human. Bioinformatics (Oxford, England), 27 (10), 1390-1396. Descargado de https://doi.org/ 10.1093/bioinformatics/btr127 doi: 10.1093/bioinformatics/btr127.Leal Alaya, L. G. (2013). Desarrollo de una metodología estadística aplicada a la construcción y comparación de redes de coexpresión génica. Universidad Nacional de Colombia, Sede-Bogotá.Leandro, G. T. (2018). Medidas de centralidad en redesurbanas con datos . Universidad de Alicante, España.Lewis, N. E., Cho, B.-K., Knight, E. M., y Palsson, B. O. (2009). Gene expression profiling and the use of genome-scale in silico models of escherichia coli for analysis: Providing context for content. Journal of Bacteriology, 191 (11), 3437-3444. Descargado de https://jb.asm.org/content/191/11/3437 doi: 10.1128/JB.00034-09.Leybovich, I., Puzis, R., Stern, R., y Reuben, M. (2018). Focused sana: Speeding up network alignment. En Socs.Li, W., Dai, C., Zhou, X. J., Tseng, G., Ghosh, D., y Zhou, X. J. (2015). Integrative analysis of many biological networks to study gene regulation. , 68-87. doi: 10.1017/CBO9781107706484.004.Li, W., Liu, C.-C., Zhang, T., Li, H., Waterman, M. S., y Zhou, X. J. (2011). Integrative analysis of many weighted co-expression networks using tensor computation.Lotero-Vélez, L., y Hurtado, R. (2014). Vulnerabilidad de redes complejas y aplicaciones al transporte urbano: Una revisi on de la literatura. Revista EIA, 11 , 67.Louzada, V., Ara ujo, N., Andrade, J. S., y Herrmann, H. (2013). Breathing synchronization in interconnected networks. Scientific Reports, 3.Mamano, N., y Hayes, W. B. (2017, 02). SANA: simulated annealing far outperforms many other search algorithms for biological network alignment. Bioinformatics, 33 (14), 2156-2164. Descargado de https://doi.org/10.1093/bioinformatics/btx090 doi: 10.1093/bioinformatics/btx090.Michael, C. (2018). Implementación del método de alineamiento de redesgedevo en R. Universidad Nacional de Colombia, Sede-Bogotá.Michoel, T., y Nachtergaele, B. (2012). Alignment and integration of complex networks by hypergraph-based spectral clustering. Physical review. E, Statistical, nonlinear, and soft matter physics, 056111.Mutwil, M., Klie, S., Tohge, T., Giorgi, F., Wilkins, O., Campbell, M., Persson, S. (2011, 03). Planet: Combined sequence and expression comparisons across plant networks derived from seven species. The Plant cell , 23 , 895-910. doi:10.1105/tpc.111.083667 Newman, E. J. (2010). Networks: An introduction (1.a ed.). Oxford University Press.Newman, M. (2002, 12). Assortative mixing in networks. Physical review letters, 89 , 208701. doi: 10.1103/PhysRevLett.89.208701.Otte, E., y Rousseau, R. (2002, 12). Social network analysis: A powerful strategy, also for the information sciences. Journal of Information Science, 28 , 441-453. doi: 10.1177/016555150202800601.Perkins, A., y Langston, M. (2009, 10). Threshold selection in gene co-expression networks using spectral graph theory techniques. BMC bioinformatics, 10 Suppl 11 , S4. doi: 10.1186/1471-2105-10-S11-S4.Porter, M. (2018). What is a multilayer network. Notices of the American Mathematical Society, 65 , 1419-1423.Reed, J., Famili, I., Thiele, I., y Palsson, B. (2006). Towards multidimensional genome annotation. Nature Reviews Genetics, 7 , 130-141.Singh, V., Rana, R., y Singhal, R. (2013, 04). Analysis of repeated measurement data in the clinical trials. Journal of Ayurveda and integrative medicine, 4 , 77-81. doi: 10.4103/0975-9476.113872.Soranzo, N., Bianconi, G., y Altafini, C. (2007a, 05). Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics, 23 (13), 1640-1647. Descargado de https://doi.org/10.1093/bioinformatics/btm163 doi: 10.1093/bioinformatics/btm163.Soranzo, N., Bianconi, G., y Altafini, C. (2007b, 05). Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics, 23 (13), 1640-1647. Descargado de https://doi.org/10.1093/bioinformatics/btm163 doi: 10.1093/bioinformatics/btm163.Welsh, E., Eschrich, S., Berglund, A., y Fenstermacher, D. (2013, 05). Iterative rankorder normalization of gene expression microarray data. BMC bioinformatics, 14 , 153. doi: 10.1186/1471-2105-14-153.YANG, S. (2013). Networks: An introduction by m. e. j. newman. The Journal of Mathematical Sociology, 37 (4), 250-251. Descargado de https://doi.org/ 10.1080/0022250X.2012.744247 doi: 10.1080/0022250X.2012.744247.Zanin, M., y Lillo, F. (2013). Modelling the air transport with complex networks: A short review. The European Physical Journal Special Topics, 215 , 5-21.InvestigadoresORIGINAL1032450483.2020.pdf1032450483.2020.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf1484414https://repositorio.unal.edu.co/bitstream/unal/80644/6/1032450483.2020.pdfa48989eaa62964b7ab7c7766f182ba07MD56LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/80644/3/license.txt8153f7789df02f0a4c9e079953658ab2MD53THUMBNAIL1032450483.2020.pdf.jpg1032450483.2020.pdf.jpgGenerated Thumbnailimage/jpeg3665https://repositorio.unal.edu.co/bitstream/unal/80644/7/1032450483.2020.pdf.jpg6e6512559b14382d4c0f30572068c5c0MD57unal/80644oai:repositorio.unal.edu.co:unal/806442023-07-30 23:03:26.492Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Comparación de la representación multiplex y monoplex de redes de co-expresión génica

Publicaciones similares