Combining the predictive power of deep learning with the interpretability of cell signaling networks

Nikolaus Fortelny and Christoph Bock of CeMM showed the usefulness of knowledge-primed neural networks (KPNNs) for the interpretation of single-cell RNA-seq data. They expect that the use of deep learning on biological networks will also be relevant in other areas of biomedicine analysing big data sets, including metabolomics, proteomics and cellular or cognitive networks.

Published in Genome Biology

Nikolaus Fortelny and Christoph Bock

Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data