Deep-learning model predicts how fruit flies form, cell by cell
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
Cells
3 Questions: On biology and medicine’s “data revolution”
Professor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.
Read MoreHow to more efficiently study complex treatment interactions
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
Read MoreCellLENS reveals hidden patterns in cell behavior within tissues, offering deeper insights into cell heterogeneity — vital for advancing cancer immunotherapy.
Read MoreWith 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.
Read MoreNovel 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.
Read MoreFragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.
Read MoreAI 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.
Read MoreA 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.
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