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T1 Deep Learning to Analyze RNA-Seq Gene Expression Data
A1 Urda, D.
A1 Montes-Torres, J.
A1 Moreno, F.
A1 Franco, L.
A1 Jerez, J. M.
A1 Rojas, I
A1 Joya, G
A1 Catala, A
K1 Deep learning
K1 RNA-Seq
K1 Personalized medicine
K1 Machine Learning
K1 Biomarkers discovery
AB Deep learning models are currently being applied in several areas with great success. However, their application for the analysis of high-throughput sequencing data remains a challenge for the research community due to the fact that this family of models are known to work very well in big datasets with lots of samples available, just the opposite scenario typically found in biomedical areas. In this work, a first approximation on the use of deep learning for the analysis of RNA-Seq gene expression profiles data is provided. Three public cancer-related databases are analyzed using a regularized linear model (standard LASSO) as baseline model, and two deep learning models that differ on the feature selection technique used prior to the application of a deep neural net model. The results indicate that a straightforward application of deep nets implementations available in public scientific tools and under the conditions described within this work is not enough to outperform simpler models like LASSO. Therefore, smarter and more complex ways that incorporate prior biological knowledge into the estimation procedure of deep learning models may be necessary in order to obtain better results in terms of predictive performance.
PB Springer international publishing ag
SN 978-3-319-59146-9
SN 0302-9743
YR 2017
FD 2017-01-01
LK http://hdl.handle.net/10668/18619
UL http://hdl.handle.net/10668/18619
LA en
DS RISalud
RD Apr 11, 2025