9 citations · 11 across the 5 of their papers we have counts for
5 papers
Data Driven Perspectives on Knot Theory
Pawel Dlotko, Davide Gurnari, Radmila Sazdanovic
Data science offers a powerful tool to understand objects in multiple sciences. In this paper we utilize concept of data science, most notably topological data analysis, to extend…
ClusterGraph: a new tool for visualization and compression of multidimensional data
Paweł Dłotko, Davide Gurnari, Mathis Hallier +1
Understanding the global organization of complicated and high dimensional data is of primary interest for many branches of applied sciences. It is typically achieved by applying di…
Probing omics data via harmonic persistent homology
Davide Gurnari, Aldo Guzmán-Sáenz, Filippo Utro +3
Identifying molecular signatures from complex disease patients with underlying symptomatic similarities is a significant challenge in the analysis of high dimensional multi-omics d…
Euler Characteristic Curves and Profiles: a stable shape invariant for big data problems
Paweł Dłotko, Davide Gurnari
Tools of Topological Data Analysis provide stable summaries encapsulating the shape of the considered data. Persistent homology, the most standard and well studied data summary, su…
Mapper-type algorithms for complex data and relations
Paweł Dłotko, Davide Gurnari, Radmila Sazdanovic
Mapper and Ball Mapper are Topological Data Analysis tools used for exploring high dimensional point clouds and visualizing scalar-valued functions on those point clouds. Inspired…