GREmLN, a new kind of AI model, is trained in “molecular logic” and could unlock new frontiers in cancer research.
Cancer often begins with a mistake — a few genes misguided by a mutation, or a set of instructions misread. When a few genes go awry, it can result in consequences that cascade through dozens, even hundreds, of other genes. Researchers are puzzled by this complex domino effect and hope to use AI to help understand how this transformation of identity and behavior occurs.
For decades, biologists have tried to study these transformations using gene expression data — snapshots of which genes are active in individual cells. Tools like single-cell RNA sequencing now offer incredibly rich data, enabling scientists to compare the molecular activity of healthy cells to diseased ones, cell by cell. But while scientists can see which genes are turned “on” or “off”, they still struggle to effectively target these mutations.
The promise is clear. With models like GREmLN, we’re moving from descriptive biology to predictive biology. From mapping what is, to simulating what could be.
In recent years, scientists have turned to AI approaches like machine learning to try to untangle this question, but most machine learning tools still aren’t built to answer it. As they’re built today, AI tools are pattern matchers, not meaning makers. The tools recognize that certain gene activity patterns correlate with disease, but can’t describe how the disease emerged, or what to do about it. That’s because traditional AI models don’t think like cells. They think like computers.
GREmLN — short for Gene Regulatory Embedding-based Large Neural model — doesn’t try to reshape biology to fit AI. Instead, it reshapes AI to fit biology.
