Please use this identifier to cite or link to this item:
http://hdl.handle.net/10609/98407
Title: | Modelització de dades òmiques amb autoencoders |
Author: | Masip Masip, Jordi |
Tutor: | Reverter, Ferran Vegas Lozano, Esteban |
Others: | Sánchez-Pla, Alex Ventura, Carles |
Abstract: | The purpose of this work is to replicate and explore the use of a stacked auto-encoder based artificial neural network to model omic data. Omic data analysis has required the development of new analysis tools, given their great dimensionality. Deep learning tools have brought improvements to this analysis challenge. The work of Xie, et al. (1) showed the superiority of a multi-layer perceptron based on stacked autoencoders model (MLP-SAE) to study the relationship between single nucleotide polymorphisms (SNP) and genetic expression (measured using high-throughput sequencing [HTSeq]), when compared with other available methods. |
Keywords: | autoencoder omics data deep learning |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jun-2019 |
Publication license: | http://creativecommons.org/licenses/by-sa/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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jmasipmasTFM0619memòria.pdf | Memòria del TFM | 2,04 MB | Adobe PDF | View/Open |
codi_i_resultats_Memoria_Treball.zip | 20,56 MB | Unknown | View/Open |
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