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dc.contributor.author
Gallo, Cristian Andrés
dc.contributor.author
Carballido, Jessica Andrea
dc.contributor.author
Ponzoni, Ignacio
dc.contributor.other
Mourad, Elloumi
dc.contributor.other
Zomaya, Albert
dc.date.available
2021-08-18T18:26:54Z
dc.date.issued
2013
dc.identifier.citation
Gallo, Cristian Andrés; Carballido, Jessica Andrea; Ponzoni, Ignacio; Inference of Gene Regulatory Networks based on Association Rules; Wiley; 2013; 803-838
dc.identifier.isbn
978-1-118-61711-3
dc.identifier.uri
http://hdl.handle.net/11336/138460
dc.description.abstract
This chapter focuses on gene regulation and the ways that transcriptome data can be used to unravel the complex relationships between the genes that comprise a gene regulatory network (GRN). In particular, it describes the main topics that must be considered in the field of association rule (AR) mining for reverse engineering of GRNs and presents the state-of-the art techniques currently available in the literature. The chapter presents the central concepts about AR mining for GRN reconstruction together with various other relevant issues. It reviews different data-mining approaches used for AR inference. The data-mining approaches are frequent-itemset-based methods, classification and regression tree-based approaches, Bayesian Networks, and Boolean networks. Over the past few years, other approaches were proposed that do not correspond to the previous classification. However, these methods can also be used to infer gene ARs from microarray data. These techniques are clustering, pairwise methods, and support vector machine methods.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
GENE REGULATORY NETWORKS
dc.subject
ASSOCIATION RULES
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MICROARRAY ANALYSIS
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
Inference of Gene Regulatory Networks based on Association Rules
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/bookPart
dc.type
info:ar-repo/semantics/parte de libro
dc.date.updated
2021-07-27T15:03:32Z
dc.journal.pagination
803-838
dc.journal.pais
Estados Unidos
dc.journal.ciudad
Nueva Jersey
dc.description.fil
Fil: Gallo, Cristian Andrés. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
dc.description.fil
Fil: Carballido, Jessica Andrea. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina
dc.description.fil
Fil: Ponzoni, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Planta Piloto de Ingeniería Química. Universidad Nacional del Sur. Planta Piloto de Ingeniería Química; Argentina. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación; Argentina
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1002/9781118617151.ch36
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118617151.ch36
dc.conicet.paginas
1192
dc.source.titulo
Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological DatBa
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