Fully unraveling the mysteries of biology is one of the most impactful technical challenges of our time and is only possible due to the current acceleration in AI.
We started this research lab to tackle this problem with a simple goal: unravel ALL biological mysteries. We, somewhat short-sightedly, call our lab GPTomics. While we use many transformers, that name no longer fits fully as achieving this goal will require a broad set of technologies, but we'll still use it to remind us of the start.
We’re taking a multi-path approach to understanding biology by both building new models to simulate biological systems at different scales, and customizing general-purpose tools (e.g., agents) to improve how we research and discover patterns in biology. Our plan is to move as fast as possible so humanity can benefit from what we learn.
We truly believe this is the moment and we are the generation that can unravel biology down to its core principles and build a reductionist foundation that holds up to the rigor of falsifiable science.
We plan to build in public for as long as we can. If you have ideas, suggestions, or want to contribute, submit a PR or contact us.
BioJEPA v0.4 Technical Report
PDFThis report presents v0.4 of BioJEPA-AC (Biological Joint-Embedding Predictive Architecture - Action Conditioned), a joint-embedding predictive architecture that uses a shared latent space to learn cell dynamics and perturbation response.
BioJEPA
GitHubApplying JEPA-based architectures to biology: building an action-conditioned “world model” for cells inspired by V-JEPA 2-AC, aiming for a digital simulator that predicts how cell states respond to perturbations.
BioSkills
GitHubA collection of skills that guide AI coding agents (Claude Code, Codex, Gemini) through common bioinformatics tasks, with code patterns, best practices, and examples across analyses from sequence work to single-cell and population genetics.
Contact
If you have something to tell us, email us at gptomics@gmail.com, or on Twitter/X @gptomics.