A new machine learning approach developed by researchers at MIT and Harvard University could help scientists more efficiently design experiments to engineer cells into new states. This could have a significant impact on the development of new therapies for cancer, regenerative medicine, and other diseases.
The new approach leverages the cause-and-effect relationship between factors in a complex system, such as genome regulation, to prioritize the best intervention in each round of sequential experiments. This allows scientists to identify optimal interventions with fewer trials, which can reduce experimental costs and accelerate the development of new therapies.
The researchers tested their approach on real biological data designed to mimic a cellular reprogramming experiment and found that it was more efficient and effective than traditional methods.
