Stanford researchers have found a way to predict an individual's remaining lifespan directly from routinely collected longitudinal electronic health records, enabling faster and more accurate decision-making for life insurance underwriting, reinsurance, and clinical planning.
Researchers at Stanford have developed ComBind, a computational platform that improves prediction of how drug molecules bind to their protein targets by combining structural modeling with readily available binding data from other molecules.
Researchers at Stanford have developed FiberFold, a computational tool enabling the rapid analysis of 3D chromatin architecture in conjunction with chromatin accessibility, CTCF binding, CpG methylation, and underlying genetic architecture.
Stanford researchers have developed Screen-GPT, an AI-powered multi-agent platform that automates CRISPR genetic screening by integrating diverse biological data to design libraries and prioritize targets through transparent, explainable, and scalable workflows.
Stanford researchers have developed a novel representation learning model that improves data-driven learning by incorporating more relevant data relationships. This approach significantly enhances both model performance and inference accuracy.
Stanford researchers have invented a unified AI architecture that integrates foundational models (FMs) with AI techniques for efficient analysis of fMRI data in psychiatric disorders.
Stanford researchers have formulated a first in line framework called EcoTyper which systematically profiles the tumor microenvironment (TME) cell states in multiple solid tumor types, providing a platform for effective personalized cancer decisions.