Researchers at Stanford have developed methods of diagnosing and treating colorectal cancer based on the discovery of genetic aberrations indicative of a patient's risk of metastasis.
Stanford researchers have formulated a statistical model to determine the risk of breast cancer recurrence with unprecedented accuracy in women 5 – 20 years after initial diagnosis.
Researchers at Stanford University have identified a small molecule tryptase inhibitor for treatment of severe allergies. Mast cells are a part of the innate and adaptive immune response. Mast Cell activation results in release of granules containing tryptases.
Dr. Curt Scharfe and colleagues have developed RUSPseq, a method for next generation molecular testing originally conceived to diagnose metabolic disorders in newborns.
Multiplexed analysis of biological components is critical for classifying molecular subtypes of heterogeneous tumors to provide patient-specific therapies.
Stanford researchers have developed a multi-omics method for predicting the strength and durability of immune responses to vaccines shortly after vaccination. The COVID-19 pandemic was a grave demonstration of the threat pandemics pose to global public health.
Stanford researchers in The Tang Group have developed a reproducible, high throughput device that dices tissue into uniformly sized sub-millimeter sample fragments.
Stanford scientists have invented a method that can determine the gestational age of a fetus by testing the mother's urine using metabolomics profiling and machine learning.
Researchers at Stanford and the Chan Zuckerberg Biohub have developed a transcriptomic characterization of human endometrium and identified specific gene signatures for use in evaluating endometrial samples for one or more menstrual cycle events.
Researchers at Stanford have developed the SNAIL-RCA method for inexpensive and efficient multiplexed detection of single RNA molecules in single cells.
Stanford researchers have applied large-scale proteomic platforms to identify biomarkers that can be used to diagnose uveal melanoma and subtype eye tumors according to their gene expression profile (GEP) class or PRAME status.
Scientists at Stanford have developed a machine learning program with broad potential for diagnostic applications which analyzes mass spectrometry data profiling metabolites in a patient sample ("metabolomics" data) and predicts infection status.