Joint environment-proteome embedding model using VICReg (Variance-Invariance-Covariance Regularization) self-supervised learning, applied to the TARA Oceans metagenomic dataset. This model aligns environmental and Pfam protein domain representations in a shared 32-dimensional latent space.
This model represents an exploratory methodological approach deposited for transparency and reproducibility. The XGBoost bidirectional framework (TARA-XGBoost-Bidirectional) was retained as the primary modeling approach in the ELF-NET study.
The negative R² under spatial CV reflects the MLP architecture's sensitivity to distribution shift on spatially distinctive held-out basins (Mediterranean, mid-Pacific), a known limitation of shallow neural networks on small tabular datasets (N ~ 1,100). This is an architecture confound, not evidence against the Pfam alignment signal itself.
Repository Contents
Directory
Contents
checkpoints/
24 model checkpoints (4 hyperparameter configurations x 6 ocean basin folds)
scripts/
Core training code (train_world_model.py, vicreg_loss.py, world_model.py)
results/
Per-fold metrics, training curves, hyperparameter sweep results, permutation tests
Bardes, A., Ponce, J., & LeCun, Y. (2022). VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning. ICLR 2022.
Authors
David R. Nelson, Kourosh Salehi-Ashtiani
New York University Abu Dhabi
Citation
bibtex
1@article{nelson2026elfnet,
2 title = {Coupling of oceanographic state to the dark proteome: a foundation for genome-informed marine productivity modeling},
3 author = {Nelson, David Roy and Plouviez, Maxence and Daakour, Sarah and Jaiswal, Ashish and Fu, Weiqi and Amin, Shady A. and Salehi-Ashtiani, Kourosh},
4 journal = {Forthcoming},
5 year = {2026}
6}
