With AI, researchers predict the location of virtually any protein within a human cell
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Cells
Novel method detects microbial contamination in cell cultures
Ultraviolet light “fingerprints” on cell cultures and machine learning can provide a definitive yes/no contamination assessment within 30 minutes.
AI system predicts protein fragments that can bind to or inhibit a target
FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.
AI model deciphers the code in proteins that tells them where to go
Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.
A causal theory for studying the cause-and-effect relationships of genes
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
A more effective experimental design for engineering a cell into a new state
By focusing on causal relationships in genome regulation, a new AI method could help scientists identify new immunotherapy techniques or regenerative therapies.
AI models are powerful, but are they biologically plausible?
A new study bridging neuroscience and machine learning offers insights into the potential role of astrocytes in the human brain.
Bacterial injection system delivers proteins in mice and human cells
With further development, the programmable system could be used in a range of applications including gene and cancer therapies.