AI for Cell-Ag Researchers
A starting point for wet-lab researchers — biologists, biochemists, bioprocess engineers, and cell-ag practitioners — building foundational understanding of the AI / ML methods catalogued throughout the library. Start with the learning playlists, then follow the cross-links into agentic-AI and foundation-model work. (Approaching from the computer-science side instead — learning cellular agriculture? See the companion Cellular Agriculture for AI researchers primer.)
Learn the fundamentals
Educational playlists for the audience approaching machine learning from the biology side. Start at the top; these are entry points, not exhaustive references.
- PlaylistAI Fundamentalsintroductory AI concepts, intended as a first entry point.
- PlaylistMachine Learningbroader ML topics covered as a non-intimidating tour through the field.
- PlaylistNeural Networks / Deep Learningneural-network fundamentals through transformer architecture (the foundation of ChatGPT and modern LLMs).
- PlaylistAI Models Explainedoverview of common AI model architectures and where each is used.
- PlaylistAI Agents Explainedoverview of AI agent systems; pairs naturally with the LLMs / AI Agents row in the Papers matrix.
Go deeper
Once you have the basics, these are the higher-level talks and references.
- In CAAIL AI Agents & Foundation Models for Biology talksagentic AI, foundation models, and language-model-based scientific reasoning, many from the Broad Institute's MIA series.
- In CAAIL Virtual Cell Initiative & single-cell foundation modelsArc Institute's State / Stack program, the open Virtual Cell Challenge, and Cell2Sentence.
- In CAAIL Curated bibliographies & awesome listscommunity-maintained indexes for the AI / single-cell / bioinformatics literature.