Matthew Hirn

This paper introduces GEFMAP, a geometric deep learning method to predict metabolic flux from scRNA-seq data, estimating reaction rates in metabolic networks.

The paper introduces node2vec+, an improved version of node2vec that incorporates edge weights in biased random walks, showing better performance on weighted gene networks.

The paper introduces PHATE, a visualization method that captures nonlinear structure in biological data, showing it preserves patterns better than existing tools and reveals novel insights in scRNA-seq data.