Vast amounts of valuable research data remain unused, trapped in labs or lost to time. Frontiers aims to change that with FAIR² Data Management, a groundbreaking AI-driven system that makes datasets reusable, verifiable, and citable. By uniting curation, compliance, peer review, and interactive visualization in one platform, FAIR² empowers scientists to share their work responsibly and gain recognition.
Scientists at Skoltech developed a new mathematical model of memory that explores how information is encoded and stored. Their analysis suggests that memory works best in a seven-dimensional conceptual space — equivalent to having seven senses. The finding implies that both humans and AI might benefit from broader sensory inputs to optimize learning and recall.
A new AI tool called DOLPHIN exposes hidden genetic markers inside single cells, enabling earlier detection and more precise treatment choices. It also sets the stage for building virtual models of cells to simulate disease and drug responses.
A powerful new AI tool called Diag2Diag is revolutionizing fusion research by filling in missing plasma data with synthetic yet highly detailed information. Developed by Princeton scientists and international collaborators, this system uses sensor input to predict readings other diagnostics can’t capture, especially in the crucial plasma edge region where stability determines performance. By reducing reliance on bulky hardware, it promises to make future fusion reactors more compact, affordable, and reliable.
A new AI model from NYU Abu Dhabi predicts solar wind days in advance with far greater accuracy than existing methods. By analyzing ultraviolet solar images, it could help protect satellites, navigation systems, and power grids from disruptive space weather events.
Scientists at Mount Sinai have created an artificial intelligence system that can predict how likely rare genetic mutations are to actually cause disease. By combining machine learning with millions of electronic health records and routine lab tests like cholesterol or kidney function, the system produces "ML penetrance" scores that place genetic risk on a spectrum rather than a simple yes/no. Some variants once thought dangerous showed little real-world impact, while others previously labeled uncertain revealed strong disease links.