New machine learning algorithm from Stanford team leads to the discovery of hundreds of potential drug targets for ALS

In this study, Dr. Michael Snyder and his team found nearly seven hundred risk genes related to ALS (Amyotrophic Lateral Sclerosis) using a novel machine learning method, leading to a 5-fold increase in recovered heritability. Their findings give light to...

Longitudinal data study shows gut microbiome diversity changes at the subspecies level during antibiotic treatment

Published on the August cover of Genome Research, a team led by Dr. Morteza Roodgar used a novel method of “read cloud” DNA sequencing to obtain resolution at the subspecies level. In this study, the team longitudinally tracked a patient’s gut...

Webinar: Prof. Eric Topol on The Future of Precision Medicine – Opportunities and Challenges

We invited Prof. Eric Topol, the Director and Founder of Scripps Research Translational Institute, as a guest speaker to a new course in the Department of Genetics, Cloud Computing for Biology and...

Real-time alerting system for COVID-19 using wearable data – Stanford preprint available now

We are very excited to share with you our recent preprint for Phase 2 of our COVID-19 wearable study. Before we dive in, if you are part of our 3,246 study cohort, or have helped us make this study known to the world in any way, we are beyond grateful for...

Smartwatch data can predict blood test results, new Stanford study reports

A smartwatch can signal physiological changes, such as a change in red blood cell count, as well as early signs of dehydration, anemia and illness, according to a new study led by researchers at Stanford Medicine. Have you ever wondered if your wearable device...