Deep learning applied to biological data: protein language models, single-cell foundation models, medical imaging, and generative models for molecules.
A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.
Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.
Walk through a published tutorial in PyTorch + Hugging Face and reproduce its results on the provided sample data.
Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README
Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.
Pick one study from UniProt / AlphaFold DB / OpenProblems, reproduce the headline result, and write a short technical note on what you found.
Deliverable: GitHub repo + 3-page PDF write-up
Move from tutorials to original analyses on real questions. Start showing your work publicly.
Fine-tune ESM or a similar PLM on a small downstream task (e.g. solubility or localization) and report metrics vs. a baseline.
Deliverable: Training notebook + model card + metrics report on Hugging Face Hub
Package your work, target real roles, and prepare to interview in the sector.
Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.
Deliverable: Live portfolio URL + PDF CV + cover letter template
Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.
Trains and fine-tunes models on biological sequences, structures, or images.
Builds infrastructure and pipelines for training/serving biological ML models.
Answer a short profile and the AI builder will tailor every phase — pace, hours, tools, and resources — to your background and goals in this sector.
Build my personalized roadmap