A new deep-learning system called Atomic Rotationally Equivariant Scorer (ARES) significantly improves the prediction of RNA structures over previous artificial intelligence (AI) models.
Researchers in the Herzenberg laboratory at Stanford University have patented a method to quantify antigens during flow cytometry without the use of calibrators.
Stanford inventors have developed an early-stage screening method to diagnose abdominal aortic aneurysms (AAA). AAA is a common cardiovascular disease with high prevalence in European men 65 years and above.
Stanford scientists have created a statistical framework for interpreting next generation sequencing data which obviates the need for sequence alignment references in the most common and fundamental problems in genomics.
Stanford scientist has developed a computational method that extracts quantitative imaging features that reproducibly describe lesion phenotypes associated with treatment response and clinical outcomes in cancer.
Stanford researchers have developed a geometric deep learning based novel method to aid in identification and discovery of novel drug scaffolds as well as to optimize known scaffolds, as a means to combat the major challenge in drug discovery.
Stanford inventors have created a novel, interactive, highly scalable computational approach for representing dynamic brain activity as a network for use in clinical settings.
Stanford researchers developed a framework called 'Hummingbird' that predicts the cheapest, fastest and most efficient configurations to execute genomics pipelines on the cloud.
Stanford researchers have developed software that offers a solution to presenting tasks in a clinical magnetic resonance imaging facility to evoke specific responses within the human brain.
Stanford researchers have developed a software tool called GapMap that contains one of the most robust resource databases for families with autism, compiled via exhaustive machine-learning methods, and highlights gaps in clinical services.