Already, Rabadan is working with researchers at Columbia and other universities, exploring different cancers, from brain to blood cancers, learning the grammar of regulation in normal cells,

“The idea is that using these models, we can look at mutations and illuminate that part of the genome.”

“The vast majority of mutations found in cancer patients are in so-called dark regions of the genome. These mutations do not affect the function of a protein and have remained mostly unexplored. says Rabadan.

a term borrowed from cosmology that refers to the vast majority of the genome, which does not encode known genes—in cancer and other diseases.

The new computational methods should also allow researchers to start exploring the role of genome’s “dark matter”—

The inputs consisted of genome sequences and data showing which parts of the genome are accessible and expressed.

Xi Fu, a graduate student in Rabadan’s lab, decided to take a different approach, training a machine learning model on gene expression data from millions of cells obtained from normal human tissues.

“Previous models have been trained on data in particular cell types, usually cancer cell lines or something else that has little resemblance to normal cells,” Rabadan says.

Such information about gene expression can tell researchers the identity of the cell and how the cell performs its functions.

In the new study, Rabadan and his colleagues tried to use AI to predict which genes are active within specific cells

New AI method predicts gene expression in any cell

But the use of AI methods to predict the activities of genes and proteins inside cells has proven more difficult.

The 2024 Nobel Prize in Chemistry was awarded to researchers for their groundbreaking work in using AI to predict protein structures.

In recent years, the accumulation of massive amounts of data from cells and more powerful AI models are starting to transform biology into a more predictive science.

“It would turn biology from a science that describes seemingly random processes into one that can predict the underlying systems that govern cell behavior.”

“Having the ability to accurately predict a cell’s activities would transform our understanding of fundamental biological processes,” Rabadan says.

Traditional research methods in biology are good at revealing how cells perform their jobs or react to disturbances. But they cannot make predictions about how cells work or how cells will react to change,

These methods can effectively conduct large-scale computational experiments, boosting and guiding traditional experimental approaches,” says Raul Rabadan, professor of systems biology and senior author

“Predictive generalizable computational models allow to uncover biological processes in a fast and accurate way.

The system, described in the current issue of Nature, could transform the way scientists work to understand everything from cancer to genetic diseases.