README.md

# DevoGraph
license: mit
## Introduction
* DevoGraph is a [GSoC 2025 project](https://neurostars.org/t/gsoc-2025-project-idea-4-openworm-devoworm-devograph-350h/31949) under the administration of [INCF](https://www.incf.org/) and [DevoWorm](https://devoworm.weebly.com/). Our main goal is to provide examples and components that utlize (Temporal/Directed/...) Graph Neural Networks to model the developmental process of *[C. elegans](https://en.wikipedia.org/wiki/Caenorhabditis_elegans)*. 

## Developers
* GSoC contributors: [Jiahang Li](https://github.com/LspongebobJH/DevoGraph), [Wataru Kawakami](https://github.com/watarungurunnn/GSoC2022_submission/tree/main), [Himansuhu Chougule](https://github.com/himanshu-02/DevoGraph), [Pakhi Banchalia](https://www.github.com/Pakhi07), [Mehul Arora](https://github.com/mehular0ra), [Pakhi Banchalia](https://github.com/Pakhi07), [Sushmanth Reddy](https://github.com/sushmanthreddy/), [Jayadratha Gayen]([email protected]), [Lalith Bharadwaj Baru]([email protected]). 
* Mentors: [Bradly Alicea](https://bradly-alicea.weebly.com/), [Jesse Parent](https://jesparent.github.io/), [Himansuhu Chougule](https://github.com/himanshu-02/DevoGraph), [Mehul Arora](https://github.com/mehular0ra)
* Additional contributors: [Longhui Jiang](https://github.com/jianglonghui/DevoGraph), [Gautham Krishnan](https://github.com/gauthamk02)

## Contributions
### Jiahang Li
* 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.ipynb` to check more details. 
* Refactor codes of constructing ****directed** graphs** initially implemented by [cell-track-gnn](https://github.com/talbenha/cell-tracker-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](https://github.com/talbenha/cell-tracker-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.

### Wataru Kawakami
* worked on image processing issues (Stage 1).

### Longhui Jiang
* Refactor codes of pre-processing 2-D images(frames of videos) and converting them into location information of cells stored in .csv files (Stage 1). The re-implementation is based on [cell-track-gnn](https://github.com/talbenha/cell-tracker-gnn). 

### Sushmanth Reddy
* incorporating DevoLearn models into DevoGraph, particularly for Stage 1.

### Himanshu Chougule
* developed a customized RNN for creating graph embeddings, building out Topological Data Analysis tools and infrastructure.
  
### Mehul Arora
* developed a Hypergraph model of the embryo.

### Pakhi Banchalia
* developed applications of k-mapper for Topological Data Analysis and Neural Developmental Programs.

### Lalith Baru
* continued work on a Hypergraph model of the embryp.

### Jayadratha Gayen
* developed a temporal graph neural network (T-GNN) approach.