Sonia Rose Harris, Alexis Amano, Marcy Winget, Kelley M Skeff, Cati G Brown-Johnson
Trauma-Informed Healthcare Leadership? Evidence and opportunities from interviews with leaders during COVID-19
BMC health services research
gene regulatory networks grns encode gene regulation living organisms become critical tool understand complex biological processes however due dynamic complex nature gene regulation inferring grns scrnaseq data still challenging task existing computational methods usually focus close connections genes ignore global structure distal regulatory relationships study develop supervised deep learning framework igegrns infer grns scrnaseq data based graph embedding framework contextual information genes captured graphsage aggregates gene features neighborhood structures generate lowdimensional embedding genes k influential nodes whole graph filtered topk pooling finally potential regulatory relationships genes predicted stacking cnns compared nine competing supervised unsupervised methods method achieves better performance six timeseries scrnaseq datasets method igegrns implemented python using pytorch machine learning library freely available httpsgithubcomdhudblabigegrns