A1393
Title: Multivariate techniques for high-dimensional analysis of genomic data
Authors: Francisco Javier Esquivel - University of Granada (Spain) [presenting]
Juan Manuel Praena Fernandez - Universidad de Granada (Spain)
Jose Luis Romero Bejar - University of Granada (Spain)
Abstract: Molecular biology and medicine are living in an era of exponential advances in which the emergence of the so-called omics sciences is the result of the existence of new technology that allows us to see where it was previously impossible. There are as many omics sciences as there are biological or molecular elements that can be studied by these technologies. Genomics is the study of an organism's genome, i.e. its DNA, and how that information is applied. All living things, from unicellular bacteria to multicellular organisms, such as plants and animals, have DNA. The study of genetics helps to understand how genes work and what impact they have on diseases. Therefore, genomics, together with statistical analysis techniques of complex data, are essential to personalized medicine and early diagnosis of diseases. A review of the state of the art related to multivariate techniques commonly used in this context is performed, and different illustrations of application are addressed using different packages of the Bioconductor project.
