| dc.contributor.author | Canal-Alonso, Ángel | |
| dc.contributor.author | Egido, Noelia | |
| dc.contributor.author | Jiménez, Pedro | |
| dc.contributor.author | Prieto Tejedor, Javier | |
| dc.contributor.author | Corchado Rodríguez, Juan Manuel | |
| dc.date.accessioned | 2023-10-04T11:11:47Z | |
| dc.date.available | 2023-10-04T11:11:47Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://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.iso | eng | es_ES |
| dc.subject | Genomics | es_ES |
| dc.subject | Precision medicine | es_ES |
| dc.subject | Workflow management system | es_ES |
| dc.subject | Cloud computing | es_ES |
| dc.subject.mesh | Genomics | * |
| dc.title | Review of state-of-the-art algorithms for genomics data analysis pipelines | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.subject.unesco | 1203.04 Inteligencia Artificial | es_ES |
| dc.subject.unesco | 2409 Genética | es_ES |
| dc.relation.projectID | CCTT3/20/SA/0003 | es_ES |
| dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
| dc.type.hasVersion | info:eu-repo/semantics/draft | es_ES |
| dc.subject.decs | genómica | * |