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dc.contributor.authorCanal-Alonso, Ángel
dc.contributor.authorEgido, Noelia
dc.contributor.authorJiménez, Pedro
dc.contributor.authorPrieto Tejedor, Javier 
dc.contributor.authorCorchado Rodríguez, Juan Manuel 
dc.date.accessioned2023-10-04T11:11:47Z
dc.date.available2023-10-04T11:11:47Z
dc.date.issued2022
dc.identifier.urihttp://hdl.handle.net/10366/153150
dc.description.abstract[EN]The advent of big data and advanced genomic sequencing technologies has presented challenges in terms of data processing for clinical use. The complexity of detecting and interpreting genetic variants, coupled with the vast array of tools and algorithms and the heavy computational workload, has made the development of comprehensive genomic analysis platforms crucial to enabling clinicians to quickly provide patients with genetic results. This chapter reviews and describes the pipeline for analyzing massive genomic data using both short-read and long-read technologies, discussing the current state of the main tools used at each stage and the role of artificial intelligence in their development. It also introduces DeepNGS (deepngs.eu), an end-to-end genomic analysis web platform, including its key features and applications.es_ES
dc.language.isoenges_ES
dc.subjectGenomicses_ES
dc.subjectPrecision medicinees_ES
dc.subjectWorkflow management systemes_ES
dc.subjectCloud computinges_ES
dc.subject.meshGenomics *
dc.titleReview of state-of-the-art algorithms for genomics data analysis pipelineses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.subject.unesco1203.04 Inteligencia Artificiales_ES
dc.subject.unesco2409 Genéticaes_ES
dc.relation.projectIDCCTT3/20/SA/0003es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/draftes_ES
dc.subject.decsgenómica *


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