Using neural networks based on epigenomic maps for predicting the transcriptional regulation measured by CRISPR/Cas9

[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.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.TFG

Aprendizaje automático para predecir el coeficiente de expresión de la proteina HIS3

Las relaciones epistásicas han sido un gran enigma en el campo de la biología desde sus inicios, conocer como reacciona una proteina dado un set de mutaciones es vital para poder generar nuevos experimentos y avanzar en la investigación. Por esto proponemos un nuevo método basado en redes neuronales que demuestra predecir con un gran porcentaje de acierto el fitness relaticvo final de la proteina HIS3 dado una serie de mutaciones desde un original.Barberá Mourelle, A. (2018). Aprendizaje automático para predecir el coeficiente de expresión de la proteina HIS3. http://hdl.handle.net/10251/111115TFG