A groundbreaking AI model developed by Columbia University computational biologists can accurately predict the activity of genes within human cells, offering unprecedented insights into cellular functions and potentially revolutionizing the understanding of diseases like cancer.
In the same way that ChatGPT understands human language, a new AI model developed by computational biologists captures the language of cells to accurately predict their activities. Researchers at Columbia University Vagelos College of Physicians and Surgeons can accurately predict the activity of genes within any human cell, essentially revealing the cell's inner mechanisms.
The system, described in the current issue of Nature, could transform the way scientists work to understand everything from cancer to genetic diseases. 'Predictive generalizable computational models allow to uncover biological processes in a fast and accurate way. 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 of the new paper. 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, like a cancer-causing mutation. 'Having the ability to accurately predict a cell's activities would transform our understanding of fundamental biological processes,' Rabadan says.'It would turn biology from a science that describes seemingly random processes into one that can predict the underlying systems that govern cell behavior.' 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. The 2024 Nobel Prize in Chemistry was awarded to researchers for their groundbreaking work in using AI to predict protein structures
AI Cell Biology Predictive Modeling Cancer Research Genetic Diseases
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