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dc.contributor.authorCanal-Alonso, Ángel
dc.contributor.authorJiménez, Pedro
dc.contributor.authorEgido, Noelia
dc.contributor.authorPrieto Tejedor, Javier 
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2023-10-04T11:40:55Z
dc.date.available2023-10-04T11:40:55Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10366/153156
dc.description.abstract[EN]DNA sequencing is one of the fields that has advanced the most in recent years within clinical genetics and human biology. However, the large amount of data generated through next generation sequencing (NGS) techniques requires advanced data analysis processes that are sometimes complex and beyond the capabilities of clinical staff. Therefore, this work aims to shed light on the possibilities of applying hybrid algorithms and explainable artificial intelligence (XAI) to data obtained through NGS. The suitability of each architecture will be evaluated phase by phase in order to offer final recommendations that allow implementation in clinical sequencing workflowses_ES
dc.language.isoenges_ES
dc.subjectNext-Generation sequencinges_ES
dc.subjectExplainable Artificial Intelligencees_ES
dc.subjectHybrid Algorithmses_ES
dc.subject.meshAlgorithms *
dc.subject.meshSequence Analysis, DNA *
dc.titleApplication of hybrid algorithms and Explainable Artificial Intelligence ingenomic sequencinges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
dc.subject.unesco2410.07 Genética Humanaes_ES
dc.relation.projectIDCCTT3/20/SA/0003es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES
dc.subject.decsalgoritmos *
dc.subject.decsanálisis de secuencias de ADN *


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