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Research funding for the digital orientation of Life Sciences

Funding of "Deep Learning Methods for Association Studies of Transcriptomic and Systemic Dynamics in Morphogenetically Active Tissues".

As part of the call for proposals "Big Data in the Life Sciences of the Future" of the Lower Saxony Ministry of Science and Culture (MWK) and the Volkswagen Foundation, the project "Deep Learning Methods for Association Studies of Transcriptomic and Systemic Dynamics in Morphogenetically Active Tissues" at the BCCN Göttingen is funded for three years.

Image: Information flow during the formation of organs and tissues; here: stages of embryonic development of drosophila. Photo: Philip J. Keller (HHMI)

In this initiative, scientists from information theory, theoretical neuroscience, transcriptomics and cell and developmental biology are working together to combine imaging and expression data for the first time and thus understand the relationships between the gene expression of individual cells and the behavior of cell clusters. The aim is to automate dynamic tissue reconstruction from large-area live imaging data using deep learning to identify and transcriptomically analyze single cells at key embryonic sites in real time.

Coordinators of the project are Prof. Dr. Fred Wolf, Max Planck Institute for Dynamics and Self-Organization Göttingen/ BCCN Göttingen, and Prof. Dr. Michael Wibral, Department of Data-Driven Analysis of Biological Networks at the University of Göttingen. Also involved are researchers from developmental biochemistry and the UMG transcriptome analysis laboratory. The funding applied for amounts to almost one million euros over three years.

Text: after press release of UMG Göttingen