Welcome to DevoLearn!

Accelerate data driven developmental biology research with computational learning models

With Google Summer of Code 2020 almost complete, we can debut our latest endeavor: DevoLearn! DevoLearn is a platform that incorporates a computational analysis platform for embryos of different species, with an annotated collection of secondary datasets (DevoZoo) and educational tools.

While the first part (DevoLearn 0.2.0) is brand new, the other two components (species-specific models and DevoZoo) are revamped versions of resources we have created over the course of the past three years. Thanks to Mayukh Deb and Ujjwal Singh for their efforts this Summer, and Vinay Varma, Siddharth Yadav, Asmit Singh, and Bradly Alicea for their past efforts leading up to this point.

Now let’s take a look at the the DevoLearn umbrella:

DevoLearn umbrella. Click to enlarge.

The entire project is hosted as a Github project. The DevoLearn software is also hosted on PyPi, and is available as an open-source software package there. The species-specific models (which includes the existing OpenDevoCell resource) are hosted as Herokuapps, while the DevoZoo (access to secondary dat and educational resources) is hosted as a set of Github pages. Aside from the educational component, we encourage people to use DevoLearn for conducting their own Machine Learning and Deep Learning analyses.

Please also feel free to contribute content to DevoLearn! Contributions in the areas of data science tutorials and other types of quantitative analysis are welcome. We are looking for data science tutorials, educational materials that merge ML/DL and biology, and perhaps even novel analytical models. Thanks to Krishna Katyal for pushing a tutorial on Linux command line basics.

Github repository link Click to enlarge.

Over the past few years, the DevoWorm group has been trying to develop new ways of quantifying development. Some of this has been sponsored by Google Summer of Code (embryo cell segmentation) over the past few years, and this summer (thanks to Mayukh) we were able to revisit topics such as worm movement and embryo networks. The DevoLearn software package is a pre-trained model that enables the discovery of meta-features, or features that transcend the typical features of biological image processing.

PyPi project description link Click to enlarge.

Ujjwal’s contributions to DevoLearn include a new resource called DevoWormAI and a retooling of the DevoZoo and educational web interface that has slowly been developed over the past few years. Ultimately, this will also include two educational endeavors from 2019: OpenWorm/DevoWorm Curriculum and DevoWormML. These educational initiatives tie together Machine Learning, Developmental Biology, and Complex Systems Theory. Future directions include working towards theoretical models of quantitative embryo data, such as a Laplacian description of the embryo.

DevoWormAI link Click to enlarge.

DevoZoo 2.0 link Click to enlarge.

So explore and make the most out of this resource! We would be interested in your proposals for additions or next steps.

Posted by Bradly Alicea