Using AI, MIT researchers identify a new class of antibiotic candidates
These compounds can kill methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that causes deadly infections.
These compounds can kill methicillin-resistant Staphylococcus aureus (MRSA), a bacterium that causes deadly infections.
Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.
Five MIT faculty, along with seven additional affiliates, are honored for outstanding contributions to medical research.
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.
A one-week summer program aims to foster a deeper understanding of machine-learning approaches in health among curious young minds.
The challenge involves more than just a blurry JPEG. Fixing motion artifacts in medical imaging requires a more sophisticated approach.
“FrameDiff” is a computational tool that uses generative AI to craft new protein structures, with the aim of accelerating drug development and improving gene therapy.
BioAutoMATED, an open-source, automated machine-learning platform, aims to help democratize artificial intelligence for research labs.
The machine-learning algorithm identified a compound that kills Acinetobacter baumannii, a bacterium that lurks in many hospital settings.
With the artificial intelligence conversation now mainstream, the 2023 MIT-MGB AI Cures conference saw attendance double from previous years.
MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.
Seven researchers, along with 14 additional MIT alumni, are honored for significant contributions to engineering research, practice, and education.
Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.
New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.