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dc.contributor.author Ponzoni, Ignacio
dc.contributor.author Nueda, María José
dc.contributor.author Tarazona, Sonia
dc.contributor.author Götz, Stefan
dc.contributor.author Montaner, David
dc.contributor.author Dussaut, Julieta Sol
dc.contributor.author Dopazo, Joaquín
dc.contributor.author Conesa, Ana
dc.date.available 2017-02-03T18:59:16Z
dc.date.issued 2014-03
dc.identifier.citation Ponzoni, Ignacio; Nueda, María José; Tarazona, Sonia; Götz, Stefan; Montaner, David; et al.; Pathway network inference from gene expression data; BioMed Central; Bmc Systems Biology; 8; supl. 2; 3-2014; 1-17
dc.identifier.issn 1752-0509
dc.identifier.uri http://hdl.handle.net/11336/12460
dc.description.abstract Background: The development of high-throughput omics technologies enabled genome-wide measurements of the activity of cellular elements and provides the analytical resources for the progress of the Systems Biology discipline. Analysis and interpretation of gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of biological systems requires a further level of analysis that addresses the characterization of the interaction between functional modules. Results: We present a novel computational methodology to study the functional interconnections among the molecular elements of a biological system. The PANA approach uses high-throughput genomics measurements and a functional annotation scheme to extract an activity profile from each functional block -or pathway- followed by machine-learning methods to infer the relationships between these functional profiles. The result is a global, interconnected network of pathways that represents the functional cross-talk within the molecular system. We have applied this approach to describe the functional transcriptional connections during the yeast cell cycle and to identify pathways that change their connectivity in a disease condition using an Alzheimer example. Conclusions: PANA is a useful tool to deepen in our understanding of the functional interdependences that operate within complex biological systems. We show the approach is algorithmically consistent and the inferred network is well supported by the available functional data. The method allows the dissection of the molecular basis of the functional connections and we describe the different regulatory mechanisms that explain the network's topology obtained for the yeast cell cycle data.
dc.format application/pdf
dc.language.iso eng
dc.publisher BioMed Central
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Pathways
dc.subject Pathway Network
dc.subject Gene Expression
dc.subject Bioinformatics
dc.subject.classification Ciencias de la Información y Bioinformática
dc.subject.classification Ciencias de la Computación e Información
dc.subject.classification CIENCIAS NATURALES Y EXACTAS
dc.title Pathway network inference from gene expression data
dc.type info:eu-repo/semantics/article
dc.type info:ar-repo/semantics/artículo
dc.type info:eu-repo/semantics/publishedVersion
dc.date.updated 2017-02-02T14:07:25Z
dc.journal.volume 8
dc.journal.number supl. 2
dc.journal.pagination 1-17
dc.journal.pais Reino Unido
dc.journal.ciudad Londres
dc.description.fil Fil: Ponzoni, Ignacio. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Planta Piloto de Ingeniería Química (i); Argentina
dc.description.fil Fil: Nueda, María José. Universidad de Alicante; España
dc.description.fil Fil: Tarazona, Sonia. Centro de Investigaciones Principe Felipe; España. Universidad de Valencia; España
dc.description.fil Fil: Götz, Stefan. Centro de Investigaciones Principe Felipe; España
dc.description.fil Fil: Montaner, David. Centro de Investigaciones Principe Felipe; España
dc.description.fil Fil: Dussaut, Julieta Sol. Universidad Nacional del Sur. Departamento de Ciencias e Ingenieria de la Computacion. Laboratorio de Investigación y Desarrollo en Computacion Cientifica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil Fil: Dopazo, Joaquín. Centro de Investigaciones Principe Felipe; España
dc.description.fil Fil: Conesa, Ana. Centro de Investigaciones Principe Felipe; España
dc.journal.title Bmc Systems Biology
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/url/http://bmcsystbiol.biomedcentral.com/articles/10.1186/1752-0509-8-S2-S7
dc.relation.alternativeid info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1186/1752-0509-8-S2-S7


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info:eu-repo/semantics/openAccess Excepto donde se diga explícitamente, este item se publica bajo la siguiente descripción: Creative Commons Attribution 2.5 Unported (CC BY 2.5)