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Informations Commons Day at UCSF
Source:
BCHSI
April 28, 2022

As researchers who depend on data, join us on Monday, May 9 to learn how to deepen your research inquiry.

UCSF Researchers Use Gene Expression Data to Map Cell Types in the CNS
Source:
BCHSI
April 12, 2022

Ashish Raj co-led the development of a computational pipeline, Matrix Inversion and Subset Selection.

Single-Cell Seq Study Uncovers Blood Cell Type Features for Lupus
Source:
Science
April 8, 2022

Jimmie Ye, et al, published in Science on how they deployed mux-seq to profile 1.2M immune cells for lupus.


 

A Biomedical Open Knowledge Network Harnesses the Power of AI
Source:
AI MAGAZINE
March 31, 2022

Sharat Israni, et al and collaborators publish on knowledge networks identified by NSC as a core component of AI frameworks.


 

Information Commons Wynton
Source:
BCHSI
December 20, 2021

Integrating De-Identified Medical Images, Notes, EHR Data and More


 

Atul Butte Testifies before Congress
Source:
BCHSI
December 8, 2021

Butte articulated priorities to Energy & Commerce Subcommittee on Health “Future of Biomedicine” Hearing.


 

Precision Medicine to Address Prostate Cancer in Veterans
Source:
BCHSI
November 16, 2021

Franklin Huang: Providing precision oncology solutions for veterans using data science and focusing on disparities.

UCSF SPOKE featured at NSF Convergence Accelerator Expo
Source:
BCHSI
July 6, 2021

Learn about UCSF’s knowledge network project and other innovative work supported by NSF Convergence Accelerator.

AIMBE honors two Bakar Faculty
Source:
BCHSI
March 19, 2021

Katie Pollard and Duan Xu are inducted into the prestigious American Institute for Medical and Biological Engineering College of Fellows.

Introducing the enhanced March of Dimes database for preterm birth research
Source:
BCHSI
November 24, 2020

Marina Sirota helps establish a new and improved database for preterm birth research as part of the March of Dimes Prematurity Research Network.

CT Lung
Source:
BERKELEY LAB COMPUTING SCIENCES
June 1, 2020

Dani Ushizima explores algorithms and a data analysis pipeline to help accurately distinguish COVID-19 abnormalities in CT scans and chest X-rays.