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Bibliografía básica
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Love MI, Huber W, Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology
Chen Y, Lun AAT, Smyth GK (2016). From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research
Enis Afgan, Dannon Baker, Bérénice Batut, Marius van den Beek, Dave Bouvier, Martin ?ech, John Chilt (2018). The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Research
TCGA Consortium (2022). The Cancer Genome Atlas. https://portal.gdc.cancer.gov/
NCBI Gene Expression Omnibus (2022). NCBI Gene Expression Omnibus. https://www.ncbi.nlm.nih.gov/geo/
Michael Love, Wolfgang Huber y Simon Anders. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology
Malachi Griffith y col. (2015). Informatics for RNA Sequencing: A Web Resource for Analysis on the Cloud. Plos Computational Biology
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Bibliografía complementaria
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Liñares-Blanco J., Fernandez-Lozano C., Seoane JA y López-Campos G. (2022). Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes. Frontiers in Microbiology
Fernández-Edreira D., Liñares-Blanco J. y Fernandez-Lozano C. (2021). Machine Learning analysis of the human infant gut microbiome identifies influential species in type 1 diabetes. Expert Systems with Applications
Liñares-Blanco, J., Gestal, M., Dorado, J., y Fernandez-Lozano, C. (2019). Differential gene expression analysis of RNA-seq data using machine learning for cancer research. Machine Learning Paradigms. Learning and Analytics in Intelligent Systems. Vol 1. Springer, Cham.
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Asignaturas