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dc.contributor.advisorHaddow, Paulinenb_NO
dc.contributor.authorLeon Pozo, Pedronb_NO
dc.date.accessioned2014-12-19T13:38:13Z
dc.date.available2014-12-19T13:38:13Z
dc.date.created2012-02-23nb_NO
dc.date.issued2011nb_NO
dc.identifier505167nb_NO
dc.identifierntnudaim:6750nb_NO
dc.identifier.urihttp://hdl.handle.net/11250/252776
dc.description.abstractThis work appears to complement an existingproject, Bio-inpired reverse engineering of regula-tory networks [STH09], proposes a new algorithminspired in the artificial development technique per-forming reverse engineering over regulatory networks.The present project studies that article addressingpossible weaknesses and scalability issues. Neverthe-less, during the investigation some updates have beenperformed over the algorithm, improving the previ-ous results in some scenarios. Moreover DARBNshave been used as representation, looking for an alter-native update schema and its possible improvementsover results. Lastly, software reusable tools have beenimplemented and documented to allow additional ex-periments.5nb_NO
dc.languageengnb_NO
dc.publisherInstitutt for datateknikk og informasjonsvitenskapnb_NO
dc.subjectntnudaim:6750no_NO
dc.subjectMTDT datateknikkno_NO
dc.subjectDatateknikkno_NO
dc.titleBio-inspired Reverse Engineering of Regulatory Networks: A Revised Approachnb_NO
dc.typeMaster thesisnb_NO
dc.source.pagenumber40nb_NO
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi, matematikk og elektroteknikk, Institutt for datateknikk og informasjonsvitenskapnb_NO


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