Research News

Research & Results

Expanding evidence leads to new pharmacogenomics payer coverage

March 1, 2021. Teri Klein (pictured here) and colleagues released this Comment, with bearing on reimbursement for pharmacogenomics, in Genetics in Medicine. 


Stanford Medicine launches in-house service for whole genome sequencing

February 11, 2021. DBDS faculty member Euan Ashley is featured in this Stanford Medicine News release about "a new Stanford Medicine service [that] analyzes patients’ entire genetic code for information that could reveal the roots of diseases. The service is based on whole genome sequencing, a test that maps all of an individual’s DNA."


Silicon Valley Prescribes ‘Big Data' to Combat COVID-19

February 10, 2021. By analyzing health records of past COVID-19 patients at Stanford Hospital, DBDS faculty Nigam Shah (pictured here) and team discovered that, in many cases, patients released from ICU and sent home did just as well as those who remained in the ICU.


Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression

February 9, 2021. First author Ruilin Li (Rivas Lab member, pictured here) and DBDS community members Yosuke Tanigawa, Johanne Justesen, Trevor Hastie, Robert Tibshirani, and Manuel Rivas contributed the results of their new survival analysis to Bioinformatics.

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“Even if you can do it, should you?” Researchers talk combating bias in artificial intelligence

February 3, 2021. "As artificial intelligence becomes increasingly common in several areas of public life — from policing to hiring to healthcare — AI researchers Timnit Gebru, Michael Hind, James Zou (DBDS) and Hong Qu came together to criticize Silicon Valley’s lack of transparency and advocate for greater diversity and inclusion in decision making. The event, titled 'Race, Tech & Civil Society: Tools for Combating Bias in Datasets and Models,' was sponsored by the Stanford Center on Philanthropy and Civil Society, the Center for Comparative Studies in Race and Ethnicity and the Stanford Institute for Human-Centered Artificial Intelligence (HAI)."


How Does Mixup Help Robustness and Generalization?

January 22, 2021. Members of the Zou Group, including PhD Student Amirata Ghorbani and DBDS faculty member James Zou, released the results of their latest preprint to arXiv.org. The study shows how a strategy called Mix-up improves the reliability of machine learning models. It has been accepted as a spotlight paper at the International Conference on Learning Representations (ICLR 2021). Stanford HAI released a story about the new study on January 27, 2021.


Polygenic risk modeling with latent trait-related genetic components

January 14, 2021. Rivas Lab members Matthew Aguirre (pictured here), Yosuke Tanigawa, and Guhan Venkataraman, and DBDS faculty members Rob Tibshirani, Trevor Hastie, and Manuel Rivas, showcase the results of their recent research in this new  European Journal of Human Genetics article.