• Deep Learning to Analyze RNA-Seq Gene Expression Data 

      Urda, Daniel; Montes-Torres, Julio; Moreno, Fernando; Franco, Leonardo; Jerez-Aragonés, José Manuel (Springer, 2017)
      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 ...
    • Learning Bayesian Networks for Student Modeling 

      Millán, Eva; Belmonte-Martinez, Maria Victoria; Jiménez, Guiomar; Pérez-de-la-Cruz, José-Luis (2015-07-03)
      In the last decade, there has been a growing interest in using Bayesian Networks (BN) in the student modelling problem. This increased interest is probably due to the fact that BNs provide a sound methodology for this ...
    • Machine learning models to search relevant genetic signatures in clinical context 

      Urda, Daniel; Luque-Baena, Rafael; Franco, Leonardo; Sánchez-Maroño, Noelia; Jerez-Aragonés, José Manuel (2017-06-26)
      Clinicians are interested in the estimation of robust and relevant genetic signatures from gene sequencing data. Many machine learning approaches have been proposed trying to address well-known issues of this complex ...
    • A transfer-learning approach to feature extraction from cancer transcriptomes with deep autoencoders 

      López-García, Guillermo; Jerez, José M; Franco, Leonardo; Veredas-Navarro, Francisco Javier (2019-06-18)
      The diagnosis and prognosis of cancer are among the more challenging tasks that oncology medicine deals with. With the main aim of fitting the more appropriate treatments, current personalized medicine focuses on using ...