GSoC 2023 Project Idea 3.1 D-GNNs: developing DevoGraph for computational developmental biology (OpenWorm Foundation) (350 h)

Graph neural networks (GNNs) are a potentially powerful method for discovering connectivity in geometrically complex datasets. The DevoWorm group has developed an open-source GNN framework for embryogenetic data called DevoGraph. Developmental GNNs (D-GNNs) allow us to characterize a growing network that undergoes shape transformations along with increases in size. During GSoC 2022, we developed a roadmap for progress in this area, but were not able to develop full integration with our Deep Learning-based pre-trained model (DevoLearn). Ultimately, we aim to tie our D-GNN work into the group’s work on embryo networks, developmental connectomes, and embryo differentiation.

During the project period, you will be involved in three activities: 1) refining a means to segment raw data and incorporate it into the DevoGraph pipeline, 2) refining our method for deriving graph embeddings, using techniques from topological data analysis and complex network theory, and 3) more tightly integrating DevoGraph as a network structure discovery module of DevoLearn. Achieving 1) will require refactoring CNN models and understanding biological training datasets. Activities 2) and 3) require the ability to work with mathematical models and associated algorithms. Knowledge of graph and/or network theory is helpful, but not required.

What can I do before GSoC?

You can ask one of the mentors to direct you to the data source and you can start working on it. Please feel free to join the OpenWorm Slack or attend our meetings to raise questions/discussions regarding your approach to the problem.

OpenWorm Foundation:

DevoWorm website:

DevoGraph (Github): GitHub - DevoLearn/DevoGraph

DevoWorm AI: DevoWorm.AI

Learning on Graphs (LoG) Conference Recap: learning-on-graphs-log-conference-


Skill level: Advanced

Required skills: All of our existing models are built for PyTorch, so experience with Python and

PyTorch/Tensorflow workflows is preferred. The ability to work with datasets, such as

segmenting video and generating graph visualizations is essential. An ability to build

web interfaces, UI design, basic knowledge of biology, open-source practices, and

applied mathematical tools will also be useful.

Time commitment: Full-time (350 hours)

Lead mentor: Bradly Alicea

Project website:

Backup mentors: Jiahang Li

Tech keywords: GNNs, Computational Biology, Graph Theory, PyTorch

1 Like

Hii! I am Kritika
I am currently a student studying CS, Mathematics and Computational biology. This project aligns with my interest, but I am afraid I am not skilled enough as I’ve never worked with TensorFlow or PyTorch although I do know programming in Python and this is a advanced project. Do you think I’ll be able to provide valuable input in this project?
Thank you

Hi @KritikaVerma , nice to hear from you :slight_smile:
Tagging the mentor/s to help you get started @b.alicea Thanks!

By the way, the devoworm team has a pretty active slack group. I would highly recommend going through the website of openworm/devoworm shared and get into the slack channel. You will get much direct and faster access to Bradly and the larger devoworm community.

Please join us in the OpenWorm Slack: OpenWorm Foundation on LaunchPass Join the #devoworm and #devolearn-devograph channels, and feel free to join our weekly meetings (Mondays @ 3pm UTC). Link will be posted in #devoworm before the meeting.

hello @b.alicea !
I am Viren, I’m a final year Btech student at NIT Karnataka. I’m relatively new to open source and Graph Neural Networks (I understand them in theory only). But I have a vast knowledge of deep learning, computer vision and Bio-informatics through personal and course projects. I’ve got experience in working with Py Torch/Tensorflow. I’d love to work on DevoGraph learn through the project. I’ve already cloned the repository, joined openworm channel and it would be really helpful if you could give me a task/issue to get started on
Thank you

Thank you! I would definitely join it and try learning more about the topic.

Thank you! I joined it but I am yet to receive the confirmation.