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dc.contributor.advisor | Conejero Casares, José Alberto | es_ES |
dc.contributor.advisor | Orzáez Calatayud, Diego Vicente | es_ES |
dc.contributor.author | Barberá Mourelle, Alejandro | es_ES |
dc.date.accessioned | 2016-09-13T07:34:58Z | |
dc.date.available | 2016-09-13T07:34:58Z | |
dc.date.created | 2016-07-14 | |
dc.date.issued | 2016-09-13 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/69318 | |
dc.description.abstract | [EN] Because of the great impact that the genomic editing with CRISPR/CAS9 has had in the recent years, and the great advances that it brings to biotechnology a great need of information has arisen. However researches struggle to find a definate pattern with these experiments making a very long process of trial and error to find an optimal solution for a particular experiment. With this project we intend to optimize the genomic edition with the newest advance CRISPR/Cas9, to find the optimal insertion site we design a mathematical model based on neural networks. During this process we had to deal with huge amount of information from the genome so we had to develop a way to filter and handle it efficiently. For this project we are going to focus in Arabidopsis Thaliana which is a very common plant in genomic edition and has many resources available online. | es_ES |
dc.format.extent | 45 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Universitat Politècnica de València | es_ES |
dc.rights | Reserva de todos los derechos | es_ES |
dc.subject | Genomic | es_ES |
dc.subject | Neural networks | es_ES |
dc.subject | Redes neuronales | es_ES |
dc.subject | CRISP/cas9 | es_ES |
dc.subject | Arabidopsis thaliana | es_ES |
dc.subject | Genómica | es_ES |
dc.subject.classification | MATEMATICA APLICADA | es_ES |
dc.subject.other | Grado en Ingeniería Informática-Grau en Enginyeria Informàtica | es_ES |
dc.title | Using neural networks based on epigenomic maps for predicting the transcriptional regulation measured by CRISPR/Cas9 | es_ES |
dc.type | Proyecto/Trabajo fin de carrera/grado | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada | es_ES |
dc.description.bibliographicCitation | Barberá Mourelle, A. (2016). Using neural networks based on epigenomic maps for predicting the transcriptional regulation measured by CRISPR/Cas9. http://hdl.handle.net/10251/69318. | es_ES |
dc.description.accrualMethod | TFGM | es_ES |
dc.relation.pasarela | TFGM\46100 | es_ES |