Selected Publication


Inferring population dynamics from single-cell RNA-sequencing time series data

© Nature Biotechnology / David Fischer, Helmholtz Zentrum München

We present a new machine learning model which describes the dynamics of cell development. Thanks to single-cell genomics, their destiny of cells in large populations can be analyzed. However, this method destroys the cell, which makes it difficult to draw conclusions about the dynamics of cell development. Psedodynamics, a mathematical model that estimates developmental processes from single-cell time series observations dresses this problem. The paper has been published in Nature Biotechnology.

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