D-GNNs: developing DevoGraph for computational developmental biology (OpenWorm Foundation)
Contributor: Sushmanth Reddy Mereddy
Mentors: Jesse Parent, Bradly Alicea, Jiahang Li, Mayukh Deb
DevoGraph has two stages to build a graph neural network (GNN): stage 1 is to extract centroids and we feed this to stage 2 to build a GNN. However, DevoLearn does not properly segment when cells are densely located, and thus, the volume cannot be extracted. We therefore propose a new instance-segmentation model for DevoLearn to extract densely located cells along with their volumes in time series data, as this helps with better graph embeddings at different points of time.Topological data analysis is done using different toolkits on microscopy data to extract topological features of raw data.
- Incorporate segmented raw microscopy data into DevoGraph pipeline
- Refactoring of CNN models for understanding biological training datasets
- Tighter integration of DevoGraph as a network structure