- DevoGraph is a GSoC 2022 project under the administration of INCF and DevoWorm. Our main goal is to provide examples and components that utlize (Temporal/Directed/...) Graph Neural Networks to model the developmental process of C. elegans.
- GSoC 2022 participants: Jiahang Li, Wataru Kawakami
- Mentors: Bradly Alicea, Jesse Parent
- External contributors: Longhui Jiang
- Design a KNN-based method constructing temporal graphs. The method is implemented in
./devograph/datasets/datasets.py. These temporal graphs are based on 3d positions of cell centroids and mimic cell developmental process of C. elegans. Each node represents a cell at a certain frame, and edges at the same frame connect neighbors according to KNN while edges across different frames connect mother and daughter cells. Please refer to./stage_2/stage_2.ipynbto check more details. - Refactor codes of constructing directed graphs initially implemented by cell-track-gnn. The re-implementation is in
./devograph/datasets/datasets1.py. This method gives each edge an direction implying the relationship between mother and daughter cells. - Refactor codes of a directed GNN initially implemented by cell-track-gnn. The re-implementation is in
./devograph/models/ct.py. The GNN is based on directed graphs and incorporates information of nodes and edges to aggregate messages. - Both of re-implementations above abstract the core logic, remove redundant and unrelated codes and unnecessary third-party frameworks, and finally provide easy-to-use APIs.
- Design the whole pipeline of DevoGraph presented in
./miscellaneous/GSoC 2022 22.1.pdf. - Assign tasks to other participants.
- Please refer to Wataru Kawakami to check his contributions.