This talk will focus on point-cloud data, their analysis and biological applications in which they naturally arise. In particular, I will introduce “optimal transport”, which has evolved as one of the major frameworks to meaningfully compare point-cloud data and explain how it can be incorporated into classical machine learning algorithms for further downstream analysis. This talk will explore two applications of optimal transport-based machine learning methods. First, I will explain how it can be used to understand temporal transformations and developmental processes of cells. Second, I’ll apply it to find signatures of different cancer types in gene expression profiles.

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