Welcome to Quantitative Biomedical Research Center

Meet some of the talented faculty members, staff, and students in the Quantitative Biomedical Research Center (QBRC) at UT Southwestern Medical Center.


In the QBRC, we have multiple research labs for interdisciplinary biological research. Collaboration and teamwork are our keywords here.

Research Labs

We provide online tools and packages for biological research. Our computational biology team is continually releasing software to aid with statistical analysis.


We welcome talented students, postdoctoral researchers, and faculty members to join us. We also greatly welcome any questions or comments regarding the QBRC.

Contact Us

Bioinformatics Computer Model Predicts Deadliest Lung Cancers

Newswise — DALLAS – March 8, 2017 – After evaluating more than 900 differences in the shape and structure of cancer cells, UT Southwestern Medical Center researchers developed a computer model able to predict the most deadly lung cancers based on a fraction of those features.

“This computational approach should someday make it possible for doctors to tailor the treatment of individual patients based on risk predicted by computer algorithms, for instance choosing to treat patients at higher risk more aggressively,” said Dr. Guanghua “Andy” Xiao, Professor of Population and Data Sciences and Bioinformatics and senior author of a study outlining this approach.

Guanghua Xiao, Ph.D.

Dept. of Population and Data Sciences
UT Southwestern Medical Center

Class: Biomedical and Basic Science Informatics – HI5305

Students will be introduced to a number of computational domains that are gaining an increasing importance in Health Informatics. Specific topics include Deep Learning for Healthcare, Introduction to Python I, Python II, and Introduction to R for Beginners, Level 1 & 2.

Machine Learning I will provide foundational understanding of machine learning models such as logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, artificial neural networks, and machine learning (ML) algorithms and will demonstrate how these models can solve complex problems diagnosis, image recognition, and understanding of text.

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