An assessment of gene regulatory network inference algorithms
| dc.contributor.advisor | Lopez-Kleine, Liliana | spa |
| dc.contributor.author | Zuur Pedraza, Adrián Guillermo | spa |
| dc.contributor.researchgroup | Grupo de Investigación en Bioinformática y Biología de Sistemas | spa |
| dc.date.accessioned | 2021-02-16T15:26:28Z | spa |
| dc.date.available | 2021-02-16T15:26:28Z | spa |
| dc.date.issued | 2020-07-31 | spa |
| dc.description.abstract | A conceptual issue regarding gene regulatory network (GRN) inference algorithms is establishing their validity or correctness. In this study, we argue that for this purpose it is useful to conceive these algorithms as estimators of graph-valued parameters of explicit models for gene expression data. On this basis, we perform an assessment of a selection of influential GRN inference algorithms as estimators for two types of models: (i) causal graphs with associated structural equations models (SEMs), and (ii) differential equations models based on the thermodynamics of gene expression. Our findings corroborate that networks of marginal dependence fail in estimating GRNs, but they also suggest that the strength of statistical association as measured by mutual information may be indicative of GRN structure. Also, in simulations, we find that the GRN inference algorithms GENIE3 and TIGRESS outperform competing algorithms. However, more importantly, we also find that many observed patterns hinge on the GRN topology and the assumed data generating mechanism. | spa |
| dc.description.abstract | Un problema conceptual con respecto a los algoritmos de inferencia de redes de regulación génica (RRG) es cómo establecer su validez. En este estudio sostenemos que para este objetivo conviene concebir estos algoritmos como estimadores de parámetros de modelos estadísticos explícitos para datos de expresión génica. Sobre esta base, realizamos una evaluación de una selección de algoritmos de inferencia de RRG como estimadores para dos tipos de modelos: (i) modelos de grafos causales asociados a modelos de ecuaciones estructurales (MEE), y (ii) modelos de ecuaciones diferenciales basados en la termodinámica de la expresion genica. Nuestros hallazgos corroboran que las redes de dependencias marginales fallan en la estimación de las RRG, pero también sugieren que la fuerza de la asociación estadística medida por la información mutua puede reflejar en cierto grado la estructura de las RRG. Además, en un estudio de simulaciones, encontramos que los algoritmos de inferencia GENIE3 y TIGRESS son los de mejor desempeño. Sin embargo, crucialmente, también encontramos que muchos patrones observados en las simulaciones dependen de la topología de la RRG y del modelo generador de datos. | spa |
| dc.description.degreelevel | Maestría | spa |
| dc.format.extent | 1 recurso en línea (130 páginas) | spa |
| dc.format.mimetype | application/pdf | spa |
| dc.identifier.citation | Zuur Pedraza, A. G. (2020). An assessment of gene regulatory network inference algorithms [Tesis de maestría, Universidad Nacional de Colombia]. Repositorio Institucional. | spa |
| dc.identifier.uri | https://repositorio.unal.edu.co/handle/unal/79256 | |
| dc.language.iso | eng | spa |
| dc.publisher.branch | Universidad Nacional de Colombia - Sede Bogotá | spa |
| dc.publisher.department | Departamento de Estadística | spa |
| dc.publisher.program | Bogotá - Ciencias - Maestría en Ciencias - Estadística | spa |
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| dc.rights | Derechos reservados - Universidad Nacional de Colombia | spa |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.license | Atribución-NoComercial-SinDerivadas 4.0 Internacional | spa |
| dc.rights.spa | Acceso abierto | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | spa |
| dc.subject.ddc | 500 - Ciencias naturales y matemáticas | spa |
| dc.subject.ddc | 570 - Biología | spa |
| dc.subject.ddc | 519 - Probabilidades y matemáticas aplicadas | spa |
| dc.subject.proposal | Gene regulatory network | eng |
| dc.subject.proposal | Red de regulación génica | spa |
| dc.subject.proposal | Modelo termodinámico | spa |
| dc.subject.proposal | Gene network inference | eng |
| dc.subject.proposal | Gene regulation | eng |
| dc.subject.proposal | Modelo de ecuaciones estructurales | spa |
| dc.subject.proposal | Red de relevancia | spa |
| dc.subject.proposal | Biological network | eng |
| dc.subject.proposal | Red biológica | spa |
| dc.subject.proposal | Relevance network | eng |
| dc.subject.proposal | Inferencia de redes génicas | spa |
| dc.subject.proposal | Structural equations model | eng |
| dc.subject.proposal | Regulación génica | spa |
| dc.subject.proposal | Thermodynamic model | eng |
| dc.title | An assessment of gene regulatory network inference algorithms | spa |
| dc.type | Trabajo de grado - Maestría | spa |
| dc.type.coar | http://purl.org/coar/resource_type/c_bdcc | spa |
| dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
| dc.type.content | Text | spa |
| dc.type.driver | info:eu-repo/semantics/masterThesis | spa |
| dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
| oaire.accessrights | http://purl.org/coar/access_right/c_abf2 | spa |