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Gene Signatures Research Involved in Cancer Using Machine Learning
dc.contributor.author | Liñares Blanco, Jose | |
dc.contributor.author | Fernández-Lozano, Carlos | |
dc.date.accessioned | 2019-09-13T14:32:22Z | |
dc.date.available | 2019-09-13T14:32:22Z | |
dc.date.issued | 2019 | |
dc.identifier.citation | Liñares-Blanco, J.; Fernandez-Lozano, C. Gene Signatures Research Involved in Cancer Using Machine Learning. Proceedings 2019, 21, 19. | es_ES |
dc.identifier.issn | 2504-3900 | |
dc.identifier.uri | http://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.sponsorship | Instituto de Salud Carlos III; PI17/01826 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/16 | es_ES |
dc.description.sponsorship | Red Gallega de Investigación sobre Cáncer Colorrectal; ED431D 2017/23 | es_ES |
dc.description.sponsorship | Ministerio de Economía y Competitividad; UNLC08-1E-002 | es_ES |
dc.description.sponsorship | Ministerio de Economía y Competitividad; UNLC13-13-3503 | es_ES |
dc.description.sponsorship | Ministerio de Economía y Competitividad; FJCI- 2015-26071 | es_ES |
dc.description.sponsorship | Xunta de Galicia; Ref ED431G/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | M D P I AG | es_ES |
dc.relation.uri | https://doi.org/10.3390/proceedings2019021019 | es_ES |
dc.rights | Atribución 3.0 España | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Machine learning | es_ES |
dc.subject | Cancer | es_ES |
dc.subject | Transcriptomics | es_ES |
dc.subject | TCGA | es_ES |
dc.subject | RNA-Seq | es_ES |
dc.title | Gene Signatures Research Involved in Cancer Using Machine Learning | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Proceedings | es_ES |
UDC.volume | 21 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 19 | es_ES |
dc.identifier.doi | 10.3390/proceedings2019021019 | |
UDC.conferenceTitle | 2nd XoveTIC Conference, A Coruña, Spain, 5–6 September 2019. | es_ES |