Using machine learning to identify undiagnosable cancers
A new model that maps developmental pathways to tumor cells may unlock the identity of cancers of unknown primary.
A new model that maps developmental pathways to tumor cells may unlock the identity of cancers of unknown primary.
Stacy Springs named executive director; Richard Braatz is associate faculty director.
Mathematical modeling speeds up the process of programming bacterial systems to self-assemble into desired 2D shapes.
Their swirling, clustering behavior might someday inform the design of self-assembling robotic swarms.
Insight into the way the EGF receptor sends signals into cells could help researchers design new cancer drugs that target this protein.
Researchers reveal how an algae-eating bacterium solves an environmental engineering challenge.
An anomaly-detection model developed by SMART utilizes machine learning to quickly detect microbial contamination.
Jonathan Weissman and collaborators used their single-cell sequencing tool Perturb-seq on every expressed gene in the human genome, linking each to its job in the cell.
Rapid and accurate analytical test method enhances the production of high-quality cell therapy products.
MIT neuroscientists expand CRISPR toolkit with new, compact Cas7-11 enzyme.
Researchers show they can control the properties of lab-grown plant material, which could enable the production of wood products with little waste.
MIT cell biologist and computational neuroscientist recognized for their innovative research contributions.
Cells may use this strategy to clear out toxic byproducts and give their offspring a clean slate.
Family trees of lung cancer cells reveal how cancer evolves from its earliest stages to an aggressive form capable of spreading throughout the body.
The technique can help predict a cell’s path over time, such as what type of cell it will become.