S. Prada Alonso
This work englobes a comprehensive study of the correlation of high-dimensional datasets from a topological perspective. Derived from a lack of efficient algorithms of big data analysis and motivated by the importance of finding a structure of correlations in genomics, we have developed two analytical tools inspired by the topological data analysis approach. We applied graph-theoretic and algebraic topology principles to quantify structural patterns on local correlation networks and, based on them, we proposed a network model to measure the evolution of the correlation with the aging process. Furthermore, we developed a powerful computational algorithm to analyze the correlation structure globally that could serve as a diagnostic tool. Overall, this work establishes a novel perspective of analysis and modulation of hidden correlation structures contributing to the understanding of the epigenetic processes, and that is designed to be useful for non-biological fields too.
Palabras clave: Topological data analysis, correlation, high-dimensional data, epigenetics
Programado
Sesión Invitada RSME-SEIO: Análisis Topológico de Datos
10 de junio de 2022 10:10
A16
