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

6. August 2020 2024-10-08 11:42

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

6. August 2020

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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

Doi: https://doi.org/10.1186/s13059-020-02100-5

Special Issue ‚Targeting STAT3 and STAT5 in Cancer’

Therapeutic targeting of STAT3 by monobodies

One of the leading research groups worldwide, studying signal transmission by JAKs and STATs.

JakStat Monarchies

SFB-F61: This research was funded in whole or in part by the Austrian Science Fund (FWF)

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

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