MultiNet allows to establish epigenetic patterns associated with a sample condition, providing diagnostic tools in case of disease or helping to better understand the epigenetic mechanisms behind a biological process such as aging. In particular, we present a computational algorithm called MultiNet designed to efficiently represent the correlation structure and extract substantial information from it.
To this end, we design different mathematical strategies and methodologies that cover different levels of data complexity. Inspired by the idea of topological data analysis, our objective is to study the correlation structure of certain epigenetic markers through the topological properties of the associated correlation networks or graphs, which represents a novel method to describe and model these high-dimensional structures. The study and representation of the "shape" of the data is key to extract the main underlying characteristics and thus determine its correct modeling. The application of different elements of algebraic topology to data analysis through topological data analysis (TDA) provides a new and necessary perspective to analyze and understand the structures behind complex high-dimensional data sets (or "big data"). Title: "Topological analysis of high-dimensional correlation structure data with applications in epigenetics". Seminar on Statistics and Operations Researchĭay in Classroom 9 of the Faculty of Mathematics and online through Teams. Brush strokes of theoretical contributions and several fields of practical application will be provided. The need to find influential users in social networks motivates a multidisciplinary research line whose final result, from the point of view of Statistics, is the development and implementation of several outlier detection procedures in functional data that are scalable for massive data and that are also competitive, in terms of efficiency, with the most used algorithms in the usual literature on functional data. Title: "Outlier detection for BIG functional data".
Speaker: Rosa Elvira Lillo Rodríguez (Universidad Carlos III de Madrid). Day in the Salón de Grados of the Faculty of Mathematics and online through Teams.
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