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dc.contributor.advisorInza Cano, Iñaki ORCIDes
dc.contributor.authorPoncelas, Alberto
dc.contributor.otherCiencia de la Computación e Inteligencia Artificial/Konputazio Zientzia eta Adimen Artifiziala
dc.description.abstractDNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in bioc-alzheimeres
dc.subjectDNA microarrayes
dc.subjectdata analysises
dc.subjectAlzheimer diseasees
dc.titlePreprocess and data analysis techniques for affymetrix DNA microarrays using bioconductor: a case study in Alzheimer diseasees
dc.rights.holderAttribution-NonCommercial 3.0 Unported*

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Except where otherwise noted, this item's license is described as Attribution-NonCommercial 3.0 Unported