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dc.contributor.authorBarba-González, Cristóbal
dc.contributor.authorGarcía-Nieto, José
dc.contributor.authorBenítez-Hidalgo, Antonio
dc.contributor.authorAldana-Montes, Jose Francisco 
dc.date.accessioned2018-11-05T11:07:17Z
dc.date.available2018-11-05T11:07:17Z
dc.date.created2018
dc.date.issued2018-11-05
dc.identifier.urihttps://hdl.handle.net/10630/16791
dc.description.abstractInference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the inference of a GRN is a complex optimization problem that involve processing S-System models, which include large amount of gene expression data from hundreds (even thousands) of genes in multiple time-series (essays). This complexity, along with the amount of data managed, make the inference of GRNs to be a computationally expensive task. Therefore, the genera- tion of parallel algorithmic proposals that operate efficiently on distributed processing platforms is a must in current reconstruction of GRNs. In this paper, a parallel multi-objective approach is proposed for the optimal inference of GRNs, since min- imizing the Mean Squared Error using S-System model and Topology Regularization value. A flexible and robust multi-objective cellular evolutionary algorithm is adapted to deploy parallel tasks, in form of Spark jobs. The proposed approach has been developed using the framework jMetal, so in order to perform parallel computation, we use Spark on a cluster of distributed nodes to evaluate candidate solutions modeling the interactions of genes in biological networks.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiomedicina - Investigaciónen_US
dc.subject.otherGene regulatory networksen_US
dc.subject.otherMulti-objective optimizationen_US
dc.subject.otherMetaheuristicsen_US
dc.subject.otherDistributed Computingen_US
dc.subject.otherjMetalen_US
dc.subject.otherSparken_US
dc.titleScalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform Cristoen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitleIDC 2018en_US
dc.relation.eventplaceBilbao (España)en_US
dc.relation.eventdate15-17 octubre de 2018en_US


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