Guía DocenteCurso Facultade de Informática |
Grao en Ciencia e Enxeñaría de Datos |
Asignaturas |
Xestión de Datos Ómicos e Modelización |
Fontes de información |
Datos Identificativos | 2022/23 | |||||||||||||
Asignatura | Xestión de Datos Ómicos e Modelización | Código | 614G02042 | |||||||||||
Titulación |
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Descriptores | Ciclo | Período | Curso | Tipo | Créditos | |||||||||
Grao | 2º cuadrimestre |
Cuarto | Optativa | 6 | ||||||||||
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Bibliografía básica |
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 |
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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