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dc.contributor.authorLiñares Blanco, José
dc.contributor.authorFernández-Lozano, Carlos
dc.date.accessioned2019-09-13T14:32:22Z
dc.date.available2019-09-13T14:32:22Z
dc.date.issued2019
dc.identifier.citationLiñares-Blanco, J.; Fernandez-Lozano, C. Gene Signatures Research Involved in Cancer Using Machine Learning. Proceedings 2019, 21, 19.es_ES
dc.identifier.issn2504-3900
dc.identifier.urihttp://hdl.handle.net/2183/23937
dc.description.abstract[Abstract] With the cheapening of mass sequencing techniques and the rise of computer technologies, capable of analyzing a huge amount of data, it is necessary nowadays that both branches mutually benefit. Transcriptomics, in this case, is a branch of biology focused on the study of mRNA molecules, among others. The quantification of these molecules gives us information about the expression that a gene is having at a given moment. Having information on the expression of the approximately 20,000 genes harbored by human beings is a really useful source of information for the study of certain conditions and/or pathologies. In this work, patient expression -omic data data have been used to offer a new analysis methodology through Machine Learning. The results of this methodology were compared with a conventional methodology to observe how they differed and how they resembled each other. These techniques, therefore, offer a new mechanism for the search of genetic signatures involved, in this case, with cancer.es_ES
dc.description.sponsorshipInstituto de Salud Carlos III; PI17/01826es_ES
dc.description.sponsorshipXunta de Galicia; ED431D 2017/16es_ES
dc.description.sponsorshipRed Gallega de Investigación sobre Cáncer Colorrectal; ED431D 2017/23es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; UNLC08-1E-002es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; UNLC13-13-3503es_ES
dc.description.sponsorshipMinisterio de Economía y Competitividad; FJCI- 2015-26071es_ES
dc.description.sponsorshipXunta de Galicia; Ref ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherM D P I AGes_ES
dc.relation.urihttps://doi.org/10.3390/proceedings2019021019es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectMachine learninges_ES
dc.subjectCanceres_ES
dc.subjectTranscriptomicses_ES
dc.subjectTCGAes_ES
dc.subjectRNA-Seqes_ES
dc.titleGene Signatures Research Involved in Cancer Using Machine Learninges_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleProceedingses_ES
UDC.volume21es_ES
UDC.issue1es_ES
UDC.startPage19es_ES
dc.identifier.doi10.3390/proceedings2019021019
UDC.conferenceTitle2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019.es_ES


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