A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis

SEBASTIAN EMILIO VENTURA SOTORAUL MIGUEL LUQUE HUERTASJUSTO PASTOR CASTAÑO FUENTES2026-01-292026-01-29202010.1016/j.artmed.2020.1019502-s2.0-85090023182https://www.scopus.com/inward/record.uri?eid=2-s2.https://ucocris.uco.es/handle/123456789/32210Artificial IntelligenceHealth InformaticsMedicine (miscellaneous)Artificial IntelligenceHealth InformaticsMedicine (miscellaneous)A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosisresearch article