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dc.contributor.advisorHernaez, M. (Mikel)-
dc.contributor.advisorGuruceaga, E. (Elizabeth)-
dc.creatorSerrano-Sanz, G. (Guillermo)-
dc.date.accessioned2023-02-21T07:38:51Z-
dc.date.available2023-02-21T07:38:51Z-
dc.date.issued2023-02-21-
dc.date.submitted2022-12-20-
dc.identifier.citationSERRANO, Guillermo. "From transcriptomics to proteomics: Unraveling biological knowledge via Machine Learning". Hernaez, M. y Guruceaga, E. (dirs.). Tesis doctoral. Universidad de Navarra, Pamplona, 2022.es_ES
dc.identifier.urihttps://hdl.handle.net/10171/65514-
dc.description.abstractWe start by highlighting basic concepts of both molecular biology and machine learning. This overview focuses on the key ideas that are required to comprehend the rest of the work, and thus, it does not attempt at providing a comprehensive review. We start with the basis of DNA and RNA, the genetic building bricks, until the formation of the proteins, the final actors of the genetic machinery. We also explore state-of-the-art technologies to measure those processes along with their limitations. After introducing the basic biological concepts, we will discuss the basics of machine learning methodologies and some of the most important models used in recent years to solve many biological problems.es_ES
dc.language.isoenges_ES
dc.publisherUniversidad de Navarraes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectMaterias Investigacion::Ciencias de la vida::Biociencias computacionaleses_ES
dc.subjectTranscriptomicses_ES
dc.subjectProteomicses_ES
dc.subjectMolecular biologyes_ES
dc.subjectArtificial intelligencees_ES
dc.titleFrom transcriptomics to proteomics: Unraveling biological knowledge via Machine Learninges_ES
dc.typeinfo:eu-repo/semantics/doctoralThesises_ES
dc.identifier.doi10.15581/10171/65514-

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