Mor Nitzan: Learning Diffeomorphisms for Dynamical Prototype Matching in Biological Data скачать в хорошем качестве

Mor Nitzan: Learning Diffeomorphisms for Dynamical Prototype Matching in Biological Data

Title: Learning diffeomorphisms for dynamical prototype matching to reveal processes encoded in biological data Speaker: Mor Nitzan (The Hebrew University) Abstract: Characterizing dynamical systems given limited measurements is a common challenge throughout the physical and biological sciences. However, this task is challenging, especially due to transient variability in systems with equivalent long-term dynamics. In this talk, I will discuss how we addressed this challenge by introducing smooth prototype equivalences (SPE), a framework that fits a diffeomorphism using normalizing flows to distinct prototypes - simplified dynamical systems that define equivalence classes of behavior. SPE enables classification by comparing the deformation loss of the observed sparse, high-dimensional measurements to the prototype dynamics. Furthermore, our approach enables estimation of the invariant sets of the observed dynamics through the learned mapping from prototype space to data space. Our method outperforms existing techniques in the classification of oscillatory systems and can efficiently identify invariant structures like limit cycles and fixed points in an equation-free manner, even when only a small, noisy subset of the phase space is observed. I will discuss how our method, and related approaches we have developed in recent years, can be used for the detection of biological processes like the cell cycle trajectory from high-dimensional single-cell gene expression data.