Laboratory experiments confirmed AI’s prediction. Understanding the effect of these mutations uncovers specific mechanisms that drive this disease.

With AI, the researchers predicted that the mutations disrupt the interaction between two different transcription factors that determine the fate of leukemic cells.

cancer genomics and epigenomics research program at Columbia’s Herbert Irving Comprehensive Cancer Center.

“These kids inherit a gene that is mutated, and it was unclear exactly what it is these mutations are doing,” says Rabadan, who also co-directs the

Next, the investigators showed the power of their AI system when they asked it to uncover still hidden biology of diseased cells, in this case, an inherited form of pediatric leukemia.

New AI methods reveal drivers of a pediatric cancer

After training on data from more than 1.3 million human cells, the system became accurate enough to predict gene expression in cell types it had never seen, yielding results that agreed closely with

Fu and Rabadan soon enlisted a team of collaborators, including co-first authors Alejandro Buendia, now a Stanford PhD student formerly in the Rabadan lab, and Shentong Mo of Carnegie Mellon, to train

it can be a diseased or it can be a normal cell type—and we can try to see how well we predict patterns from this information,” says Rabadan.

“Here it’s exactly the same thing: we learn the grammar in many different cellular states, and then we go into a particular condition—

These systems use a set of training data to identify underlying rules, the grammar of language, and then apply those inferred rules to new situations.

The overall approach resembles the way ChatGPT and other popular “foundation” models work.

UAE, and Carnegie Mellon University, Pittsburgh, PA), Alejandro Buendia, Anouchka P. Laurent, Anqi Shao, Maria del Mar Alvarez-Torres, Tianji Yu, Jimin Tan (New York University Grossman School of Medicine,

Authors (all from Columbia except where noted): Xi Fu, Shentong Mo (Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi,

The paper, titled “A foundational model of transcription across human cell types,” was published Jan. 8 in Nature.

It’s really a new era in biology that is extremely exciting; transforming biology into a predictive science.”

Coming on the heels of other recent advances in artificial intelligence for biology, Rabadan sees the work as part of a major trend:

By presenting novel mutations to the computer model, researchers can now gain deep insights and predictions about exactly how those mutations affect a cell.

The work also opens new avenues for understanding many diseases beyond cancer and potentially identifying targets for new treatments

and how cells change in the process of cancer development.