This project aims at applying Wasserstein Auto-Encoders (WAE) to protein function determination, in order to explore the possibility of using the framework as a guiding tool for biologists in the exploration of new protein sequences.
See the report and the poster for the project.
We were inspired by the work of Sinai et al., from the Marks Laboratory at Harvard. Instead of using the Variational Auto-Encoder framework, applied the WAE to the same task, hoping to leverage the better results from this novel method.
During this work, we wrote our own implementation of the WAE framework in Pytorch, using Maximum Mean Discrepancy (MMD) as a divergence measure between the marginal and the prior (see section 3.3 in the report for more information).
The implementation can be found here. Task-specific implementations were added the protein.py module in autoencoders/.
We used the same dataset as Sinai et al. The folder data/ contains the processed data.
The folder notebooks/ contains the notebooks that were used to train the models.
See the reference folder for a review of the literature surrounding this project.
For further reference regarding the closely related Variational Auto-Encoder framework, there are nice high-level presentations, for example by Jaan Altosaar and Jeremy Jordan.