In recent years, adaptations of topological constructions and methods to the analysis of high dimensional and large data sets have been shown to be of a great deal of value. They are particularly appropriate for data in the biomedical domain, where the presence of large amounts of noise, diverse data types, and a lack of underlying theory call for methods which have a great deal of robustness to changes in underlying metrics and data models. In this talk we will discuss some of these methods, and present some interesting examples.
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