Computational tool helps uncover gene networks of cell fate
Computational tool helps uncover gene networks of cell fate
A recently published study from the Wisconsin Institute of Discovery (WID), titled Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets integrates both computational and biological sciences by using machine learning to better understand the driving forces of cellular identity. Specifically, WID member and professor Sushmita Roy and her lab used single-cell Multi-Task Network Inference (scMTNI) to detect gene regulatory networks responsible for cell type transitions. Full article
Keywords Gene regulatory networks(GRNs), cellular reprogramming, machine learning, single-cell omics, single-cell Multi-Task Network Inference (scMTNI),
The statistical machine learning approach enables a deeper understanding of how gene regulatory networks influence cellular functions in organic matter. The researchers hope this computational tool will help other researchers interpret this type of data more effectively.