Novo Nordisk and OpenAI have announced a historic strategic collaboration to integrate generative AI across R&D, manufacturing, and global operations to bring life-saving treatments to market faster.
Eka Care partners with NVIDIA to develop India’s first offline-capable medical scribe. Discover how this AI-powered documentation solution brings secure, sovereign healthcare technology to remote clinics.
NVIDIA and Eli Lilly have unveiled a landmark $1 billion partnership to launch a first-of-its-kind AI co-innovation lab in the San Francisco Bay Area. By co-locating world-class scientists and AI engineers, the collaboration aims to “industrialize” drug discovery through advanced foundation models, robotics, and “lab-in-a-loop” workflows.
SOPHiA GENETICS (NASDAQ: SOPH) and Complete Genomics have announced a collaboration to broaden access to precision oncology testing. The partnership will integrate the AI-powered SOPHiA DDMâ„¢ platform with Complete Genomics’ new DNBSEQ-T1+ sequencing system. This will provide a streamlined, sample-to-report workflow for the renowned MSK-IMPACT® (solid tumor) and MSK-ACCESS® (liquid biopsy) assays.
Eli Lilly is building the pharmaceutical industry’s most powerful supercomputer in collaboration with NVIDIA to create an “AI factory.” This massive system, featuring over 1,000 NVIDIA DGX B300 GPUs, will leverage decades of data to train specialized AI models, dramatically accelerating drug discovery and development while running on 100% renewable electricity.
The computer program forecasts individual strands of DNA building blocks (A, T, C, and G) needed to create desired patterns of gene expression in various types of cells.
A biotech startup that was spun off from UC San Diego uses deep learning to screen for potential antibiotic candidates as a TB outbreak spreads in Kansas.
A novel method predicts how a certain DNA sequence will organize itself in the cell nucleus in a matter of minutes as opposed to days.
Physicians were required to decide on cancer patients’ treatments, initially without the aid of technology and later with AI’s support.
The group tested over 20 million individual cells using both self-supervised learning techniques, then contrasted the outcomes with those of traditional learning techniques.
